# Exploiting WiFi Channel State Information for Residential Healthcare Informatics

arXiv: https://arxiv.org/abs/1712.03401 · HTML: https://mlsanity.com/paper/1712.03401/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2017.

## Built on by (most cited first, 6 of 6)

- [Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions](https://mlsanity.com/paper/2209.04160/) (2022)
- [Estimating indoor occupancy through low-cost BLE devices](https://mlsanity.com/paper/2102.03351/) (2021)
- [MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures](https://mlsanity.com/paper/2201.04212/) (2022)
- [GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi](https://mlsanity.com/paper/2205.11945/) (2022)
- [A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition](https://mlsanity.com/paper/2205.11962/) (2022)
- [WiFi-based Spatiotemporal Human Action Perception](https://mlsanity.com/paper/2206.09867/) (2022)

## Abstract (arXiv)

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.