# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
###### Abstract
As early as 2015, researchers proposed to use frequency-modulated continuous wave radar and millimeter-wave radar technology to perceive human chest movement and gesture posture, and even recognize human emotional changes [11]. Although this method can not only achieve fine-grained human motion perception and protect users' personal privacy, it also requires special radar equipment, which is expensive and difficult to control. Then researchers proposed to use RFID readers/tags to sense human respiratory movement, sleep activity, and fitness exercise [12; 13]. Although the use of commercial RFID equipment can effectively reduce the cost of equipment, it is currently only deployed in specific applications (such as toll stations, parking lots, access control systems), and the popularity of the equipment is not enough to achieve ubiquitous real-time human motion perception. Recently, in order to achieve ubiquitous real-time human motion perception, the use of WiFi signals has emerged. Specifically, researchers directly utilize the link-layer or physical-layer attributes of ubiquitous WiFi signals, such as the Received Signal Strength Indicator (RSSI) and the physical layer Channel State Information (CSI), to obtain perceptual information related to human movement. The link-layer or physical layer properties of WiFi signals can be extracted from commercial wireless cards such as the Intel 5300 network card and the Atheors 9580 network card [14; 15]. Since the link-layer RSS measurement is susceptible to the time-varying and multipath fading of the wireless channel, the existing work mostly uses the physical layer CSI to implement contactless human motion perception. That is, for a WiFi signal with a bandwidth of 20 MHz, the receiver can use the Intel 5300 network card to extract the Channel Frequency Response (CFR) estimate of 30 OFDM subcarriers. Each CSI measurement contains the amplitude and phase information of any OFDM subcarrier, so CSI can provide wireless channel time-frequency domain change information to achieve fine-grained human motion perception. At present, the perception applications based on WiFi CSI mainly focus on human activity recognition, indoor positioning, and respiratory monitoring [16; 17; 18; 19; 20; 21]. Medical studies have shown that frequent shaking of the head during sleep of children is often closely related to physiological diseases such as rickets and eczema, while frequent shaking of the head during sleep of adults is often closely related to mental diseases such as insomnia, anxiety, and even depression. Therefore, monitoring people's head movement during sleep is an important research topic in the situational perception of smart health. Therefore, this paper proposes Wi-Senser, a human head motion-detection method based on WiFi signals. Wi-Senser directly utilizes the CSI amplitude information of ubiquitous WiFi signals to achieve contactless human head motion detection without revealing the private details of the user. Specifically, Wi-Senser firstly uses an Intel 5300 network card to extract WiFi CSI data that record changes in human head movement. Then Wi-Senser uses a filter channel consisting of a Hampel filter, a wavelet filter, and an averaging filter to filter out outliers and strong noises in the original CSI signal and retain the rising/falling edges of the movement changes. Subsequently, Wi-Senser uses a sensitivity metric combining standard deviation and kurtosis, and based on this sensitivity metric, an optimal subcarrier is selected from 30 CSI subcarriers, which has the highest sensitivity to changes in head motion and is insensitive to irrelevant environmental changes. Finally, Wi-Senser uses the peak-finding algorithm to capture the true set of peaks caused by head motion from the optimal subcarrier, so as to achieve accurate detection of human head motion.