# 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. contactiles; head movement; health monitoring; WiFi CSI; Internet of Things +
Footnote †: journal: Applied Sciences  
human motion perception. In the early days of computer vision-based human motion perception research, image-based detection methods were mainly used [4]; that is, a video stream of human activity is first recorded using a camera, then the video is segmented into frames, and the relevant image processing algorithm is finally used to detect the target human movement. In recent years, video-based human motion perception technology has become increasingly mature and widely used [5; 6]. However, the perception method based on computer vision relies on lighting conditions and cannot work in non-line-of-sight environments, and there is also a risk of leaking personal privacy information such as face, height, weight. In order to overcome the limitations of the above perception methods, such as intru-siveness, light dependence, and privacy leakage, researchers have focused on using wireless signals to implement contactless human motion perception. As early as 2010, researchers proposed to use ultrasonic signals emitted by acoustic sensors to sense the user's gait, behavioral actions, and breathing rate [7; 8; 9]. However, this method requires specialized acoustic equipment, which is expensive and difficult to deploy. Therefore, the researchers explored the direct use of acoustic sensing devices (speakers and microphones) built into smartphones to sense human movement [10]. This method can overcome the limitations of light dependence and it has a low hardware cost and is easily deployed, but acoustic signal-based sensing methods are susceptible to environmental noise and still have privacy leakage risks. With the innovation of wireless communication and intelligent computing technology, the research on human motion perception based on wireless radio frequency signals has become a research hotspot in the past five years.