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

Xu Yang,Yuan Shao,Qiang Niu,Fangyuan Xiong

doi:10.1177/1550147718805718

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.

Highlights

Falls can be depicted as the abrupt change of human body from an upright position to a lying-down position without control
fall detection (FD) systems using ambient devices leverage floor vibration caused by a fall to detect a risky situation.[2]
We introduce two data preprocessing methods to deal with the raw noisy CSI data, for the purpose of improving the robustness and accuracy of the FD system

Summary

Introduction

Falls can be depicted as the abrupt change of human body from an upright position to a lying-down position without control. FD systems using ambient devices leverage floor vibration caused by a fall to detect a risky situation.[2] Wearable sensor-based[3,4] and smartphone-based[5] techniques employ sensors to capture the changes of the acceleration or velocity. Such systems require users to carry special devices on the body and are inconvenient. Camera-based systems employ activity classification algorithms[6] of images, thereby effectively detecting a fall These systems are fundamentally limited because they are affected by obstacles or lighting conditions. The above limitations hinder the popularization and application of FD systems

Methods

Findings

Conclusion