Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
80 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Hands-On Robotics: Enabling Communication Through Direct Gesture Control (2401.09077v1)

Published 17 Jan 2024 in cs.HC and cs.RO

Abstract: Effective Human-Robot Interaction (HRI) is fundamental to seamlessly integrating robotic systems into our daily lives. However, current communication modes require additional technological interfaces, which can be cumbersome and indirect. This paper presents a novel approach, using direct motion-based communication by moving a robot's end effector. Our strategy enables users to communicate with a robot by using four distinct gestures -- two handshakes ('formal' and 'informal') and two letters ('W' and 'S'). As a proof-of-concept, we conducted a user study with 16 participants, capturing subjective experience ratings and objective data for training machine learning classifiers. Our findings show that the four different gestures performed by moving the robot's end effector can be distinguished with close to 100% accuracy. Our research offers implications for the design of future HRI interfaces, suggesting that motion-based interaction can empower human operators to communicate directly with robots, removing the necessity for additional hardware.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (46)
  1. Accuracy in optical tracking with fiducial markers: an accuracy function for ARToolKit. In Third IEEE and ACM International Symposium on Mixed and Augmented Reality. 260–261. https://doi.org/10.1109/ISMAR.2004.8
  2. Progress and prospects of the human–robot collaboration. Autonomous Robots 42, 5 (Oct. 2017), 957–975. https://doi.org/10.1007/s10514-017-9677-2
  3. Affective Touch in Human–Robot Interaction: Conveying Emotion to the Nao Robot. International Journal of Social Robotics 10, 4 (Dec. 2017), 473–491. https://doi.org/10.1007/s12369-017-0446-3
  4. Lisa Anthony and Jacob O. Wobbrock. 2010. A Lightweight Multistroke Recognizer for User Interface Prototypes. In Proceedings of Graphics Interface 2010 (Ottawa, Ontario, Canada) (GI ’10). Canadian Information Processing Society, CAN, 245–252.
  5. Lisa Anthony and Jacob O. Wobbrock. 2012. $N-Protractor: A Fast and Accurate Multistroke Recognizer. In Proceedings of Graphics Interface 2012 (Toronto, Ontario, Canada) (GI ’12). Canadian Information Processing Society, CAN, 117–120.
  6. Ameer Badr and Alia Abdul-Hassan. 2020. A Review on Voice-based Interface for Human-Robot Interaction. Iraqi Journal for Electrical and Electronic Engineering 16, 2 (Nov. 2020), 1–12. https://doi.org/10.37917/ijeee.16.2.10
  7. Ling Bao and Stephen S. Intille. 2004. Activity Recognition from User-Annotated Acceleration Data. In Pervasive Computing, Alois Ferscha and Friedemann Mattern (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 1–17.
  8. Lightweight robots in manual assembly–best to start simply. Frauenhofer-Institut für Arbeitswirtschaft und Organisation IAO, Stuttgart (2016).
  9. Socially assistive robots in elderly care: a systematic review into effects and effectiveness. Journal of the American Medical Directors Association 13, 2 (2012), 114–120. https://doi.org/10.1016/j.jamda.2010.10.002
  10. How to Guide Humans Towards Skills Improvement in Physical Human-Robot Collaboration Using Reinforcement Learning?. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 4281–4287. https://doi.org/10.1109/SMC42975.2020.9283469
  11. Human-robot perception in industrial environments: A survey. Sensors 21, 5 (2021), 1571. https://doi.org/10.3390/s21051571
  12. Maria E. Cabrera and Juan P. Wachs. 2016. Embodied gesture learning from one-shot. In 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE. https://doi.org/10.1109/roman.2016.7745244
  13. Adaptive Gesture Recognition with Variation Estimation for Interactive Systems. ACM Transactions on Interactive Intelligent Systems 4, 4 (Dec. 2014), 1–34. https://doi.org/10.1145/2643204
  14. Planning for Muscular and Peripersonal-Space Comfort During Human-Robot Forceful Collaboration. In 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids). 1–8. https://doi.org/10.1109/HUMANOIDS.2018.8624978
  15. Collaborative manufacturing with physical human–robot interaction. Robotics and Computer-Integrated Manufacturing 40 (Aug. 2016), 1–13. https://doi.org/10.1016/j.rcim.2015.12.007
  16. Acceptance of Industrial Collaborative Robots by People With Disabilities in Sheltered Workshops. Frontiers in Robotics and AI 7 (2021), 173. https://doi.org/10.3389/frobt.2020.541741
  17. Florian Echtler and Andreas Butz. 2012. GISpL. In Proceedings of the Sixth International Conference on Tangible, Embedded and Embodied Interaction. ACM. https://doi.org/10.1145/2148131.2148181
  18. Franka Emika GmbH. 2023. https://frankaemika.github.io/docs/overview.html last accessed: \AdvanceDate[-1]January 17, 2024.
  19. Rinat R. Galin and Roman V. Meshcheryakov. 2020. Human-Robot Interaction Efficiency and Human-Robot Collaboration. Springer International Publishing, Cham, 55–63. https://doi.org/10.1007/978-3-030-37841-7_5
  20. Sandra G Hart and Lowell E Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Advances in psychology. Vol. 52. Elsevier, 139–183.
  21. rob@ work: Robot assistant in industrial environments. In Proceedings. 11th IEEE International Workshop on Robot and Human Interactive Communication. IEEE, 399–404.
  22. Adaptation and transfer of robot motion policies for close proximity human-robot interaction. Frontiers in Robotics and AI (2019), 69.
  23. Robot Behavior-Based User Authentication for Motion-Controlled Robotic Systems. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 1–6.
  24. GestureAgents. In Proceedings of the 7th International Conference on Tangible, Embedded and Embodied Interaction. ACM. https://doi.org/10.1145/2460625.2460658
  25. Interactive, Collaborative Robots: Challenges and Opportunities.. In IJCAI. 18–25.
  26. Gesture interaction in virtual reality. Virtual Reality & Intelligent Hardware 1, 1 (Feb. 2019), 84–112. https://doi.org/10.3724/sp.j.2096-5796.2018.0006
  27. Two-dimensional Stroke Gesture Recognition. Comput. Surveys 54, 7 (July 2021), 1–36. https://doi.org/10.1145/3465400
  28. A survey on the design and evolution of social robots — Past, present and future. Robotics and Autonomous Systems 156 (2022), 104193. https://doi.org/10.1016/j.robot.2022.104193
  29. Human–robot collaboration in industrial applications: Safety, interaction and trust. International Journal of Advanced Robotic Systems 14, 4 (2017), 1729881417716010.
  30. Design considerations for safe human-robot collaborative workplaces. Procedia CIrP 37 (2015), 248–253.
  31. Jonathan Mumm and Bilge Mutlu. 2011. Human-Robot Proxemics: Physical and Psychological Distancing in Human-Robot Interaction. In Proceedings of the 6th International Conference on Human-Robot Interaction (Lausanne, Switzerland) (HRI ’11). Association for Computing Machinery, New York, NY, USA, 331–338. https://doi.org/10.1145/1957656.1957786
  32. Rubén E. Nogales and Marco E. Benalcázar. 2021. Hand gesture recognition using machine learning and infrared information: a systematic literature review. International Journal of Machine Learning and Cybernetics 12, 10 (July 2021), 2859–2886. https://doi.org/10.1007/s13042-021-01372-y Pascher et al. (2021) Max Pascher, Annalies Baumeister, Stefan Schneegass, Barbara Klein, and Jens Gerken. 2021. Recommendations for the Development of a Robotic Drinking and Eating Aid - An Ethnographic Study. In Human-Computer Interaction – INTERACT 2021 (2021-09-01), Carmelo Ardito, Rosa Lanzilotti, Alessio Malizia, Helen Petrie, Antonio Piccinno, Giuseppe Desolda, and Kori Inkpen (Eds.). Springer, Cham. https://doi.org/10.1007/978-3-030-85623-6_21 Pascher et al. (2023) Max Pascher, Uwe Gruenefeld, Stefan Schneegass, and Jens Gerken. 2023. How to Communicate Robot Motion Intent: A Scoping Review. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems - CHI ’23. https://doi.org/10.1145/3544548.3580857 Pini et al. (2015) Fabio Pini, Francesco Leali, and Matteo Ansaloni. 2015. A systematic approach to the engineering design of a HRC workcell for bio-medical product assembly. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 1–8. Schlömer et al. (2008) Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Max Pascher, Annalies Baumeister, Stefan Schneegass, Barbara Klein, and Jens Gerken. 2021. Recommendations for the Development of a Robotic Drinking and Eating Aid - An Ethnographic Study. In Human-Computer Interaction – INTERACT 2021 (2021-09-01), Carmelo Ardito, Rosa Lanzilotti, Alessio Malizia, Helen Petrie, Antonio Piccinno, Giuseppe Desolda, and Kori Inkpen (Eds.). Springer, Cham. https://doi.org/10.1007/978-3-030-85623-6_21 Pascher et al. (2023) Max Pascher, Uwe Gruenefeld, Stefan Schneegass, and Jens Gerken. 2023. How to Communicate Robot Motion Intent: A Scoping Review. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems - CHI ’23. https://doi.org/10.1145/3544548.3580857 Pini et al. (2015) Fabio Pini, Francesco Leali, and Matteo Ansaloni. 2015. A systematic approach to the engineering design of a HRC workcell for bio-medical product assembly. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 1–8. Schlömer et al. (2008) Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Max Pascher, Uwe Gruenefeld, Stefan Schneegass, and Jens Gerken. 2023. How to Communicate Robot Motion Intent: A Scoping Review. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems - CHI ’23. https://doi.org/10.1145/3544548.3580857 Pini et al. (2015) Fabio Pini, Francesco Leali, and Matteo Ansaloni. 2015. A systematic approach to the engineering design of a HRC workcell for bio-medical product assembly. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 1–8. Schlömer et al. (2008) Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Fabio Pini, Francesco Leali, and Matteo Ansaloni. 2015. A systematic approach to the engineering design of a HRC workcell for bio-medical product assembly. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 1–8. Schlömer et al. (2008) Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  33. Recommendations for the Development of a Robotic Drinking and Eating Aid - An Ethnographic Study. In Human-Computer Interaction – INTERACT 2021 (2021-09-01), Carmelo Ardito, Rosa Lanzilotti, Alessio Malizia, Helen Petrie, Antonio Piccinno, Giuseppe Desolda, and Kori Inkpen (Eds.). Springer, Cham. https://doi.org/10.1007/978-3-030-85623-6_21 Pascher et al. (2023) Max Pascher, Uwe Gruenefeld, Stefan Schneegass, and Jens Gerken. 2023. How to Communicate Robot Motion Intent: A Scoping Review. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems - CHI ’23. https://doi.org/10.1145/3544548.3580857 Pini et al. (2015) Fabio Pini, Francesco Leali, and Matteo Ansaloni. 2015. A systematic approach to the engineering design of a HRC workcell for bio-medical product assembly. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 1–8. Schlömer et al. (2008) Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Max Pascher, Uwe Gruenefeld, Stefan Schneegass, and Jens Gerken. 2023. How to Communicate Robot Motion Intent: A Scoping Review. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems - CHI ’23. https://doi.org/10.1145/3544548.3580857 Pini et al. (2015) Fabio Pini, Francesco Leali, and Matteo Ansaloni. 2015. A systematic approach to the engineering design of a HRC workcell for bio-medical product assembly. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 1–8. Schlömer et al. (2008) Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Fabio Pini, Francesco Leali, and Matteo Ansaloni. 2015. A systematic approach to the engineering design of a HRC workcell for bio-medical product assembly. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 1–8. Schlömer et al. (2008) Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  34. How to Communicate Robot Motion Intent: A Scoping Review. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems - CHI ’23. https://doi.org/10.1145/3544548.3580857 Pini et al. (2015) Fabio Pini, Francesco Leali, and Matteo Ansaloni. 2015. A systematic approach to the engineering design of a HRC workcell for bio-medical product assembly. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 1–8. Schlömer et al. (2008) Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Fabio Pini, Francesco Leali, and Matteo Ansaloni. 2015. A systematic approach to the engineering design of a HRC workcell for bio-medical product assembly. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 1–8. Schlömer et al. (2008) Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  35. A systematic approach to the engineering design of a HRC workcell for bio-medical product assembly. In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 1–8. Schlömer et al. (2008) Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Thomas Schlömer, Benjamin Poppinga, Niels Henze, and Susanne Boll. 2008. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  36. Gesture Recognition with a Wii Controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction (Bonn, Germany) (TEI ’08). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/1347390.1347395 Shi et al. (2012) Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jane Shi, Glenn Jimmerson, Tom Pearson, and Roland Menassa. 2012. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  37. Levels of human and robot collaboration for automotive manufacturing. In Proceedings of the Workshop on Performance Metrics for Intelligent Systems. 95–100. The contributors of scikit-learn (2023) The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  38. The contributors of scikit-learn. 2023. API documentation of sklearn.ensemble.RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html last accessed: \AdvanceDate[-1]January 17, 2024. Vatavu (2017) Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  39. Radu-Daniel Vatavu. 2017. Improving Gesture Recognition Accuracy on Touch Screens for Users with Low Vision. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4667–4679. https://doi.org/10.1145/3025453.3025941 Vatavu et al. (2012) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2012. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  40. Gestures as Point Clouds: A $P Recognizer for User Interface Prototypes. In Proceedings of the 14th ACM International Conference on Multimodal Interaction (Santa Monica, California, USA) (ICMI ’12). Association for Computing Machinery, New York, NY, USA, 273–280. https://doi.org/10.1145/2388676.2388732 Vatavu et al. (2018) Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Radu-Daniel Vatavu, Lisa Anthony, and Jacob O. Wobbrock. 2018. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  41. $Q: A Super-Quick, Articulation-Invariant Stroke-Gesture Recognizer for Low-Resource Devices. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages. https://doi.org/10.1145/3229434.3229465 Venkatnarayan et al. (2021) Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Raghav H. Venkatnarayan, Shakir Mahmood, and Muhammad Shahzad. 2021. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  42. WiFi based Multi-User Gesture Recognition. IEEE Transactions on Mobile Computing 20, 3 (2021), 1242–1256. https://doi.org/10.1109/TMC.2019.2954891 Willemse and Van Erp (2019) Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  43. Christian JAM Willemse and Jan BF Van Erp. 2019. Social touch in human–robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding. International journal of social robotics 11, 2 (2019), 285–304. Wobbrock et al. (2007) Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. 2007. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  44. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. In Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (Newport, Rhode Island, USA) (UIST ’07). Association for Computing Machinery, New York, NY, USA, 159–168. https://doi.org/10.1145/1294211.1294238 Wu et al. (2009) Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Jiahui Wu, Gang Pan, Daqing Zhang, Guande Qi, and Shijian Li. 2009. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  45. Gesture Recognition with a 3-D Accelerometer. In Ubiquitous Intelligence and Computing. Springer Berlin Heidelberg, 25–38. https://doi.org/10.1007/978-3-642-02830-4_4 Ye et al. (2020) Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437 Sean Ye, Karen Feigh, and Ayanna Howard. 2020. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
  46. Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1186–1189. https://doi.org/10.1109/RO-MAN47096.2020.9223437
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (7)
  1. Max Pascher (13 papers)
  2. Alia Saad (3 papers)
  3. Jonathan Liebers (2 papers)
  4. Roman Heger (1 paper)
  5. Jens Gerken (14 papers)
  6. Stefan Schneegass (8 papers)
  7. Uwe Gruene (1 paper)
Citations (1)
View on Semantic Scholar
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets