Exploiting WiFi channel state information for residential healthcare informatics

TY - JOUR

T1 - Exploiting WiFi channel state information for residential healthcare informatics

AU - Tan, Bo

AU - Chen, Qingchao

AU - Chetty, Kevin

AU - Woodbridge, Karl

AU - Li, Wenda

AU - Piechocki, Robert

PY - 2018/5/17

Y1 - 2018/5/17

N2 - 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 the unobtrusive nature of WiFi-based sensing. Advanced signal processing techniques can accurately extract WiFi channel status information (CSI) using commercial off-the-shelf devices or bespoke hardware. This includes phase variations, frequency shifts, and signal levels. In this article, we describe the healthcare application of Doppler shifts in the WiFi CSI caused by human activities that take place in the signal coverage area. The technique is shown to recognize different types of human activities and behavior 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 realworld scenarios.

AB - 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 the unobtrusive nature of WiFi-based sensing. Advanced signal processing techniques can accurately extract WiFi channel status information (CSI) using commercial off-the-shelf devices or bespoke hardware. This includes phase variations, frequency shifts, and signal levels. In this article, we describe the healthcare application of Doppler shifts in the WiFi CSI caused by human activities that take place in the signal coverage area. The technique is shown to recognize different types of human activities and behavior 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 realworld scenarios.

U2 - 10.1109/mcom.2018.1700064

DO - 10.1109/mcom.2018.1700064

M3 - Article

SN - 0163-6804

VL - 56

SP - 130

EP - 137

JO - IEEE Communications Magazine

JF - IEEE Communications Magazine

IS - 5

ER -