Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
149 tokens/sec
GPT-4o
9 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Comprehensive Review of Automated Data Annotation Techniques in Human Activity Recognition (2307.05988v1)

Published 12 Jul 2023 in cs.LG and cs.HC

Abstract: Human Activity Recognition (HAR) has become one of the leading research topics of the last decade. As sensing technologies have matured and their economic costs have declined, a host of novel applications, e.g., in healthcare, industry, sports, and daily life activities have become popular. The design of HAR systems requires different time-consuming processing steps, such as data collection, annotation, and model training and optimization. In particular, data annotation represents the most labor-intensive and cumbersome step in HAR, since it requires extensive and detailed manual work from human annotators. Therefore, different methodologies concerning the automation of the annotation procedure in HAR have been proposed. The annotation problem occurs in different notions and scenarios, which all require individual solutions. In this paper, we provide the first systematic review on data annotation techniques for HAR. By grouping existing approaches into classes and providing a taxonomy, our goal is to support the decision on which techniques can be beneficially used in a given scenario.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (65)
  1. Rebecca Adaimi and Edison Thomaz. 2019. Leveraging active learning and conditional mutual information to minimize data annotation in human activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 3 (2019), 1–23.
  2. Zero-shot human activity recognition using non-visual sensors. Sensors 20, 3 (2020), 825.
  3. An annotation technique for in-home smart monitoring environments. IEEE Access 6 (2017), 1471–1479.
  4. Mobeacon: An iBeacon-Assisted SmartphoneBased Real Time Activity Recognition Framework. UMBC Student Collection (2015).
  5. A public domain dataset for human activity recognition using smartphones.. In Esann, Vol. 3. 3.
  6. Benchmarking annotation procedures for multi-channel time series HAR dataset. In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 453–458.
  7. Stephanie Baker and Wei Xiang. 2023. Artificial Intelligence of Things for Smarter Healthcare: A Survey of Advancements, Challenges, and Opportunities. IEEE Communications Surveys & Tutorials (2023).
  8. A semi-automatic annotation approach for human activity recognition. Sensors 19, 3 (2019), 501.
  9. A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Surveys (CSUR) 46, 3 (2014), 33.
  10. A survey on mobile crowdsensing systems: Challenges, solutions, and opportunities. IEEE communications surveys & tutorials 21, 3 (2019), 2419–2465.
  11. Recent developments in sensors for wearable device applications. Analytical and bioanalytical chemistry 413, 24 (2021), 6037–6057.
  12. Transfer learning for activity recognition: A survey. Knowledge and information systems 36 (2013), 537–556.
  13. Collecting and disseminating smart home sensor data in the CASAS project. In Proceedings of the CHI workshop on developing shared home behavior datasets to advance HCI and ubiquitous computing research. 1–7.
  14. Automatic annotation for human activity recognition in free living using a smartphone. Sensors 18, 7 (2018), 2203.
  15. Personalized Online Training for Physical Activity monitoring using weak labels. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 567–572.
  16. Semi-automated data labeling for activity recognition in pervasive healthcare. Sensors 19, 14 (2019), 3035.
  17. Towards the automatic data annotation for human activity recognition based on wearables and BLE beacons. In 2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL). IEEE, 1–4.
  18. Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey. IEEE Access 8 (2020), 210816–210836. https://doi.org/10.1109/ACCESS.2020.3037715
  19. A smart data annotation tool for multi-sensor activity recognition. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 111–116.
  20. Indolabel: Predicting indoor location class by discovering location-specific sensor data motifs. IEEE Sensors Journal 22, 6 (2021), 5372–5385.
  21. Trong Do and Daniel Gatica-Perez. 2011. Crowdsourcing annotations for human activity recognition. Computer Communications 34, 16 (2011), 1939–1949.
  22. Wearables and the medical revolution. Personalized medicine 15, 5 (2018), 429–448.
  23. AugToAct: Scaling complex human activity recognition with few labels. In Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. 162–171.
  24. Oon Peen Gan. 2018. Automatic labeling for personalized IoT wearable monitoring. In IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2861–2866.
  25. Human activity recognition in artificial intelligence framework: A narrative review. Artificial intelligence review 55, 6 (2022), 4755–4808.
  26. HM Sajjad Hossain and Nirmalya Roy. 2019. Active deep learning for activity recognition with context aware annotator selection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1862–1870.
  27. Automatic human activity segmentation and labeling in RGBD videos. In International Conference on Intelligent Decision Technologies. Springer, 383–394.
  28. A wrist sensor sleep posture monitoring system: An automatic labeling approach. Sensors 21, 1 (2021), 258.
  29. Reducing Label Fragmentation During Time-series Data Annotation to Reduce Annotation Costs. In Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers. 328–333.
  30. Activity recognition using cell phone accelerometers. SIGKDD Explorations 12, 2 (2011), 74–82.
  31. IMUTube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 3 (2020), 1–29.
  32. The internet of things: a survey. Information systems frontiers 17 (2015), 243–259.
  33. Automatic Labeling Framework for Wearable Sensor-based Human Activity Recognition. Sensors and Materials 30, 9 (2018), 2049–2071.
  34. Bo-Yan Lin and Yu-Da Lin. 2022. A Clustering-based Feature Selection for Automatic Labeling in Human Activity Recognition. In 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech). IEEE, 308–309.
  35. Mining the User Profile from a Smartphone: a Multimodal Agent Framework.. In WOA@ AI* IA. Citeseer, 47–53.
  36. Unsupervised human activity representation learning with multi-task deep clustering. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 1 (2021), 1–25.
  37. Smart annotation tool for multi-sensor gait-based daily activity data. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 549–554.
  38. Hidden Markov model-based smart annotation for benchmark cyclic activity recognition database using wearables. Sensors 19, 8 (2019), 1820.
  39. Labels: Quantified self app for human activity sensing. In Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers. 1413–1422.
  40. HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data. In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 335–340.
  41. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine 151, 4 (2009), 264–269.
  42. Scalable semi-automatic annotation for multi-camera person tracking. IEEE Transactions on Image Processing 25, 5 (2016), 2259–2274.
  43. Designing videogames to crowdsource accelerometer data annotation for activity recognition research. In Proceedings of the Annual Symposium on Computer-Human Interaction in Play. 135–147.
  44. DCNN based human activity recognition framework with depth vision guiding. Neurocomputing 486 (2022), 261–271.
  45. Seyed Ali Rokni and Hassan Ghasemzadeh. 2018. Autonomous training of activity recognition algorithms in mobile sensors: A transfer learning approach in context-invariant views. IEEE Transactions on Mobile Computing 17, 8 (2018), 1764–1777.
  46. Multi-task self-supervised learning for human activity detection. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 2 (2019), 1–30.
  47. Co-MEAL: Cost-optimal multi-expert active learning architecture for mobile health monitoring. In Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. 432–441.
  48. Ryota Sawano and Kazuya Murao. 2020. Annotation Method for Human Activity and Device State Recognition Based on Smartphone Notification Removals. Journal of Information Processing 28 (2020), 679–688.
  49. A survey of wearable devices and challenges. IEEE Communications Surveys & Tutorials 19, 4 (2017), 2573–2620.
  50. Burr Settles. 2009. Active learning literature survey. University of Wisconsin-Madison (2009).
  51. A human-centered wearable sensing platform with intelligent automated data annotation capabilities. In Proceedings of the International Conference on Internet of Things Design and Implementation. 255–260.
  52. Maja Stikic and Bernt Schiele. 2009. Activity recognition from sparsely labeled data using multi-instance learning. In Location and Context Awareness: 4th International Symposium, LoCA 2009 Tokyo, Japan, May 7-8, 2009 Proceedings 4. Springer, 156–173.
  53. Recognizing activities and spatial context using wearable sensors. arXiv preprint arXiv:1206.6869 (2012).
  54. Annotating smart environment sensor data for activity learning. Technology and Health Care 17, 3 (2009), 161–169.
  55. Selfhar: Improving human activity recognition through self-training with unlabeled data. arXiv preprint arXiv:2102.06073 (2021).
  56. Talk, text, tag? understanding self-annotation of smart home data from a user’s perspective. Sensors 18, 7 (2018), 2365.
  57. Towards estimation of cooking complexity: Free-text annotations in the kitchen environment. In Proceedings of the 6th international Workshop on Sensor-based Activity Recognition and Interaction. 1–7.
  58. HAA4D: few-shot human atomic action recognition via 3D spatio-temporal skeletal alignment. arXiv preprint arXiv:2202.07308 (2022).
  59. Detection of unsupervised standardized gait tests from real-world inertial sensor data in Parkinson’s disease. IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021), 2103–2111.
  60. Internet of medical things (IoMT)-An overview. In 2020 5th international conference on devices, circuits and systems (ICDCS). IEEE, 101–104.
  61. Talk, text or tag?. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 123–128.
  62. Fall Detection Using Self-Supervised Pre-Training Model. In 2022 Annual Modeling and Simulation Conference (ANNSIM). IEEE, 361–371.
  63. Crowdsourcing annotations for accelerometer data collected from older adults. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2012), 1–18.
  64. Shibo Zhang and Nabil Alshurafa. 2020. Deep generative cross-modal on-body accelerometer data synthesis from videos. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers. 223–227.
  65. A survey on recent advances in human activity recognition using vision, depth, and inertial sensors. Sensors 12, 9 (2012), 12334–12374.
Citations (5)

Summary

We haven't generated a summary for this paper yet.