Exploiting WiFi Channel State Information for Residential Healthcare Informatics

TL;DR

This paper explores how WiFi channel state information, especially Doppler shifts, can be used for unobtrusive indoor activity recognition to support healthcare in residential environments.

Contribution

It introduces a novel application of WiFi CSI Doppler sensing for healthcare, demonstrating its effectiveness in recognizing human activities in assisted living settings.

Findings

WiFi CSI Doppler shifts can accurately identify human activities.

The technique is suitable for unobtrusive healthcare monitoring.

Experimental case studies validate the approach in real-world environments.

Abstract

Detection and interpretation of human activities have emerged as a challenging healthcare problem in areas such as assisted living and remote monitoring. Besides traditional approaches that rely on wearable devices and camera systems, WiFi based technologies are evolving as a promising solution for indoor monitoring and activity recognition. This is, in part, due to the pervasive nature of WiFi in residential settings such as homes and care facilities, and unobtrusive nature of WiFi based sensing. Advanced signal processing techniques can accurately extract WiFi channel status information (CSI) using commercial off-the-shelf (COTS) devices or bespoke hardware. This includes phase variations, frequency shifts and signal levels. In this paper, we describe the healthcare application of Doppler shifts in the WiFi CSI, caused by human activities which take place in the signal coverage area. The technique is shown to recognize different types of human activities and behaviour and be very suitable for applications in healthcare. Three experimental case studies are presented to illustrate the capabilities of WiFi CSI Doppler sensing in assisted living and residential care environments. We also discuss the potential opportunities and practical challenges for real-world scenarios.