WmFall: WiFi-based multistage fall detection with channel state information. (October 2018)

• Record Type: Journal Article
• Authors: Yang, Xu; Xiong, Fangyuan; Shao, Yuan; Niu, Qiang
• Is Part Of: International journal of distributed sensor networks. Volume 14:Number 10(2018)
• Publication Date: 2018-10
• Subjects: Channel state information -- fall detection -- system -- WiFi devices; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147718805718
• Languages: English
• ISSNs: 1550-1329

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.