Wireless Health Monitoring using Passive WiFi Sensing

TL;DR

This paper introduces a passive WiFi sensing system for non-invasive elderly health monitoring, accurately detecting breathing, tremors, and falls using signal processing techniques with high accuracy in various conditions.

Contribution

It presents a novel passive WiFi sensing approach utilizing phase extraction and cross-ambiguity function analysis for health activity monitoring.

Findings

87% accuracy in measuring breathing rate without obstacles

98% accuracy in fall detection

93% accuracy in tremor classification

Abstract

This paper presents a two-dimensional phase extraction system using passive WiFi sensing to monitor three basic elderly care activities including breathing rate, essential tremor and falls. Specifically, a WiFi signal is acquired through two channels where the first channel is the reference one, whereas the other signal is acquired by a passive receiver after reflection from the human target. Using signal processing of cross-ambiguity function, various features in the signal are extracted. The entire implementations are performed using software defined radios having directional antennas. We report the accuracy of our system in different conditions and environments and show that breathing rate can be measured with an accuracy of 87% when there are no obstacles. We also show a 98% accuracy in detecting falls and 93% accuracy in classifying tremor. The results indicate that passive WiFi systems show great promise in replacing typical invasive health devices as standard tools for health care.