MSense: Boosting Wireless Sensing Capability Under Motion Interference

Wireless signals have been widely utilized for human sensing. However, wireless sensing systems face a fundamental limitation, i.e., the wireless device must keep static during the sensing process. Also, when sensing fine-grained human motions such as respiration, the human target is required to stay stationary. This is because wireless sensing relies on signal variations for sensing. When device is moving or human body is moving, the signal variation caused by the target area (e.g., chest for respiration sensing) is mixed with the signal variation induced by device or other body parts, failing wireless sensing. In this paper, we propose MSense, a general solution to deal with motion interference from wireless device and/or human body, moving wireless sensing one step forward towards real-life adoption. We establish the sensing model by taking both device motion and interfering body motion into consideration. By extracting the effect of body and device motions through pure signal processing, the motion interference can be removed to achieve accurate target sensing. Comprehensive experiments demonstrate the effectiveness of the proposed scheme. The achieved solution is general and can be applied to different sensing tasks involving both periodic and aperiodic motions.