The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
WiFall: Device-Free Fall Detection by Wireless Networks
Abstract
Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
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From RSSI to CSI: Indoor localization via channel response
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Understanding and Modeling of WiFi Signal Based Human Activity Recognition
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Cited by
Elderly Fall Detection Systems: A Literature Survey
Wang, X., Ellul, J., & Azzopardi, G. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00071 · Cited by 380
RadHAR: Human Activity Recognition from Point Clouds Generated through a Millimeter-wave Radar
Singh, A. D., Sandha, S. S., Garcia, L., & Srivastava, M. (2019) · Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems · doi:10.1145/3349624.3356768 · Cited by 303
A Survey on Human Behavior Recognition Using Channel State Information
Wang, Z., Jiang, K., Hou, Y., Dou, W., Zhang, C., Huang, Z., & Guo, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2949123 · Cited by 149
BodyCompass: Monitoring Sleep Posture with Wireless Signals
Yue, S., Yang, Y., Wang, H., Rahul, H., & Katabi, D. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3397311 · Cited by 112
DeepCount: Crowd Counting with WiFi via Deep Learning
Liu, S., Zhao, Y., Xue, F., Chen, B., & Chen, X. (2019) · arXiv · doi:10.48550/arxiv.1903.05316 · Cited by 105
Metadata
- Authors
- Wang, Y., Wu, K., & Ni, L. M.
- Venue
- IEEE Transactions on Mobile Computing
- Published
- 2017-02-01
- Volume
- 16
- Issue
- 2
- Pages
- 581-594
- ISSN
- 1536-1233 (ISSN-L)
- OpenAlex
- W2338892592
- Cited by
- 836
- References
- 44
- FWCI
- 49.75
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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