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WmFall: WiFi-based multistage fall detection with channel state information

Author

Listed:
  • Xu Yang
  • Fangyuan Xiong
  • Yuan Shao
  • Qiang Niu

Abstract

Traditional fall detection systems require to wear special equipment like sensors or cameras, which often brings the issues of inconvenience and privacy. In this article, we introduce a novel multistage fall detection system using the channel state information from WiFi devices. Our work is inspired by the fact that different actions have different effects on WiFi signals. By fully analyzing and exploring the channel state information characters, the falling actions can be distinguished from other movements. Considering that falling and sitting are very similar to each other, a special method is designed for distinguishing them with deep learning algorithm. Finally, the fall detection system is evaluated in a laboratory, which has 89% detection precision with false alarm rate of 8% on the average.

Suggested Citation

  • Xu Yang & Fangyuan Xiong & Yuan Shao & Qiang Niu, 2018. "WmFall: WiFi-based multistage fall detection with channel state information," International Journal of Distributed Sensor Networks, , vol. 14(10), pages 15501477188, October.
  • Handle: RePEc:sae:intdis:v:14:y:2018:i:10:p:1550147718805718
    DOI: 10.1177/1550147718805718
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