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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.

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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
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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. 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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. 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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. 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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). 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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). 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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. 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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). 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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. 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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
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Authors (7)
  1. Max Pascher (14 papers)
  2. Alia Saad (3 papers)
  3. Jonathan Liebers (2 papers)
  4. Roman Heger (1 paper)
  5. Jens Gerken (15 papers)
  6. Stefan Schneegass (8 papers)
  7. Uwe Gruene (1 paper)
Citations (1)
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