Abstract
Fall is a common cause of severe injuries that may lead to irreversible body damage and even death. A real-time fall monitoring system can reveal a fall in time for timely medical aid to a victim. This is particularly important in the context of mobile healthcare. Fall detection with most contemporary wearable devices relied solely on acceleration signals, often not flexible and robust enough. In this paper, we propose to deploy body signals in a multi-modality approach. Besides the common acceleration signals, we also make use of physiological signals returned by wearable devices for multiple modalities. Fall detection would not fail easily even if some acceleration signals become ineffective. Our experiment results indicate that we are able to attain an accuracy of more than 96%. An in-depth evaluation demonstrates that physiological signals can contribute in distinguishing falls from actions generating similar acceleration signals, such as jumps, sit-downs and walking-downstairs. Copyright © 2019 Association for Computing Machinery.
Original language | English |
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Title of host publication | Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services, iiWAS2019 |
Place of Publication | USA |
Publisher | Association for Computing Machinery |
Pages | 689-693 |
ISBN (Electronic) | 9781450371797 |
DOIs | |
Publication status | Published - 2019 |
Citation
Fu, E. Y., Wong, C. Y., Lau, K. T. Y., Leong, H. V., & Ngai, G. (2019). Your body signals expose your fall. In Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services, iiWAS2019 (pp. 689-693). Association for Computing Machinery. https://doi.org/10.1145/3366030.3366119Keywords
- Fall detection
- Wearable sensor
- Mobile health-care
- Physiological signals
- Multi-modality approach