Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals

Abstract

Detecting human head movement during sleep is important as it can help doctors to assess many physical or mental health problems, such as infantile eczema, calcium deficiency, insomnia, anxiety disorder, and even Parkinson’s disease, and provide useful clues for accurate diagnosis. To obtain the information of head movement during sleep, current solutions either use a camera or require the user to wear intrusive sensors to collect the image or motion data. However, the vision-based schemes rely on light conditions and raise privacy concerns. Many people, including the elderly and infants, may be reluctant to wear wearable devices during sleep. In this paper, we propose Wi-Senser, a nonintrusive and contactless smart monitoring system for detecting head movement during sleep. Wi-Senser directly reuses the existing WiFi infrastructure and exploits the fine-grained channel state information (CSI) of WiFi signals to capture the minute human head movement during sleep without attaching any sensors to the human body. Specifically, we constructed a filtering channel including a Hampel filter, wavelet filter, and mean filter to remove outliers and noises. We propose a new metric of carrier sensitivity to select an optimal subcarrier for recording the change in targeted body movement from 30 candidate subcarriers. Finally, we designed a peak-finding algorithm to capture the real peak set recording the change in human head movement. We designed and implemented Wi-Senser with just one commercial off-the-shelf (COTS) router and one laptop equipped with an Intel 5300 network interface card (NIC). We evaluated the performance of Wi-Senser with 10 volunteers (6 adults and 4 children). Extensive experiments demonstrate that Wi-Senser can achieve 97.95% accuracy for monitoring head movement during sleep. Wi-Senser provides a new solution for achieving noninvasive, continuous, and accurate detection of minute human movement without any additional cost.