Multi-person Sleeping Respiration Monitoring with COTS WiFi Devices

Abstract

Recently, non-intrusive respiration monitoring has attracted much attention. Many respiration monitoring systems using the commercial off-the-shelf WiFi devices have been developed. However, these systems mainly have difficulties in the presence of multiple persons. The difficulty generally comes from the separation of the effects of multiple persons' respiration on the received WiFi signals. Another problem is that even though the separation can be feasible with some complicated algorithms, it is still impossible to map the multiple identified respiration states to the corresponding persons. In this paper, we study the problem of multi-person sleeping respiration monitoring and try to address the above challenges. Instead of focusing on developing complicated signal processing algorithms, we take another approach: via the deployment of WiFi transceivers. The key insight comes from the WiFi Fresnel zone model, which indicates that a carefully placed WiFi transceiver may only be affected by the person in a certain location. Furthermore, we consider the sleeping movements of people as well as the sleeping posture change to improve the robustness of the system. Extensive experiments show that we can successfully estimate the respiration rate of multiple persons, with the Mean Absolute Error (MAE) of 0.5 bpm - 1 bpm.