The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
Abstract
The design of an activity recognition and monitoring system based on the eWatch, multi-sensor platform worn on different body positions, is presented in this paper.The system identifies the user's activity in realtime using multiple sensors and records the classification results during a day.We compare multiple time domain feature sets and sampling rates, and analyze the tradeoff between recognition accuracy and computational complexity.The classification accuracy on different body positions used for wearing electronic devices was evaluated.
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“They demonstrated accuracy ranging from 70% to 90% for six different ambulatory tasks [157].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [157]
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Cites
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Activity Recognition from User-Annotated Acceleration Data
Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147
Supervised and Unsupervised Discretization of Continuous Features
Dougherty, J., Kohavi, R., & Sahami, M. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50032-3 · Cited by 2,005
Unsupervised, dynamic identification of physiological and activity context in wearable computing
Krause, A., Siewiorek, D. P., Smailagic, A., & Farringdon, J. (2004) · Proceedings · doi:10.1109/iswc.2003.1241398 · Cited by 154
Self-citationeWatch: a wearable sensor and notification platform
Maurer, U., Rowe, A., Smailagic, A., & Siewiorek, D. P. (2006) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2006.24 · Cited by 146
Self-citationEmpirical Study of Design Choices in Multi-Sensor Context Recognition Systems
Bharatula, N. B., & Stäger, M. (2005) · Cited by 37
Power and Size Optimized Multi-Sensor Context Recognition Platform
Bharatula, N. B., Stäger, M., Lukowicz, P., & Tröster, G. (2005) · Proceedings · doi:10.1109/iswc.2005.42 · Cited by 19
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Ambient intelligence: Technologies, applications, and opportunities
Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2009.04.001 · Cited by 968
Human Action Recognition From Various Data Modalities: A Review
Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 719
Window Size Impact in Human Activity Recognition
Baños, O., Gálvez, J. M., Damas, M., Pomares, H., & Rojas, I. (2014) · Sensors · doi:10.3390/s140406474 · Cited by 699
Transfer learning for activity recognition: a survey
Cook, D., Feuz, K. D., & Krishnan, N. C. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-013-0665-3 · Cited by 447
Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435
Metadata
- Authors
- Maurer, U., Smailagic, A., Siewiorek, D. P., & Deisher, M.
- Affiliations
- Technical University of Munich (DE)
- Carnegie Mellon University (US)
- Intel (United States) (US)
- Publisher
- Defense Technical Information Center
- Published
- 2006-04-01
- OpenAlex
- W4235485727
- Cited by
- 266
- References
- 7
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 9 · 2023: 16 · 2022: 19 · 2021: 20 · 2020: 25 · 2019: 29 · 2018: 29 · 2017: 21 · 2016: 13 · 2015: 18 · 2014: 17 · 2013: 14 · 2012: 8
- Type
- report
- Language
- en
- Open access
- closed
- Funders
- National Science Foundation (0205266, 0203448)
- Intel Corporation
- Defense Advanced Research Projects Agency (NBCHD-03-0010)
- Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Keywords
- activity recognition, activity monitoring, wearable sensors, multi-sensor fusion, posture, time-domain features, sampling frequency, real-time classification, computational complexity, recognition accuracy
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