The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Recognizing daily activities with RFID-based sensors
Abstract
We explore a dense sensing approach that uses RFID sensor network technology to recognize human activities. In our setting, everyday objects are instrumented with UHF RFID tags called WISPs that are equipped with accelerometers. RFID readers detect when the objects are used by examining this sensor data, and daily activities are then inferred from the traces of object use via a Hidden Markov Model. In a study of 10 participants performing 14 activities in a model apartment, our approach yielded recognition rates with precision and recall both in the 90% range. This compares well to recognition with a more intrusive short-range RFID bracelet that detects objects in the proximity of the user; this approach saw roughly 95% precision and 60% recall in the same study. We conclude that RFID sensor networks are a promising approach for indoor activity monitoring.
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Cites
Fundamentals of Wireless Communication
Tse, D., & Viswanath, P. (2005) · doi:10.1017/cbo9780511807213 · Cited by 13,162
The Computer for the 21st Century
Weiser, M. (1991) · Scientific American · doi:10.1038/scientificamerican0991-94 · Cited by 10,017
Cited by
A tutorial on human activity recognition using body-worn inertial sensors
Bulling, A., Blanke, U., & Schiele, B. (2014) · ACM Computing Surveys · doi:10.1145/2499621 · Cited by 1,668
Sensor-Based Activity Recognition
Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2198883 · Cited by 1,074
A Survey on Ambient Intelligence in Healthcare
Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2262913 · Cited by 630
Energy Provisioning in Wireless Rechargeable Sensor Networks
He, S., Chen, J., Jiang, F., Yau, D. K. Y., Xing, G., & Sun, Y. (2013) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2012.161 · Cited by 624
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020) · arXiv · doi:10.48550/arxiv.2001.07416 · Cited by 314
“used for identifying object usage. When acting as object sensors rather than ambient sensors, RFID tags are needed to be attached to the target objects such as mugs, books, computers, and toothpaste [20]. In the detection phase, a worn RFID reader is also needed. Considering both the convenience and efficiency, wrist-…”
Method
Metadata
- Authors
- Buettner, M., Prasad, R., Philipose, M., & Wetherall, D.
- Venue
- Proceedings of the 11th international conference on Ubiquitous computing
- Publisher
- ACM
- Published
- 2009-09-30
- Pages
- 51-60
- OpenAlex
- W2122991029
- Cited by
- 301
- References
- 2
- FWCI
- 16.08
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Buettner, M., Prasad, R., Philipose, M., & Wetherall, D. (2009). Recognizing daily activities with RFID-based sensors. Proceedings of the 11th international conference on Ubiquitous computing. https://doi.org/10.1145/1620545.1620553
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