Wi-Monitor: Daily Activity Monitoring Using Commodity Wi-Fi

Daily activity monitoring is essential to healthy lifestyle assessment and personal healthcare, among which Wi-Fi-based solutions have attracted increasing attention due to their no-intrusive and privacy-protected characters. However, related researches are based on the assumption that there is an interval between two activities, during which the target is thought to be static. This assumption falls short of reality as human activities are performed continuously in daily life. Therefore, this article aims to design a nonintrusive and privacy-protected system, namely, Wi-Monitor, to monitor human activities in daily life. In Wi-Monitor, we first fragmentize Wi-Fi channel state information (CSI) streams into CSI bins and design a feature extraction network to extract activity fragmentation features (AFFs) from these CSI bins. From the extracted AFFs, a temporal convolutional network (TCN) is further used to capture activity continuity features (ACFs), which are used as distinguishing characteristics of continuous activities. Finally, Wi-Monitor utilizes these distinguishing characteristics to segment and recognize human activities in daily life simultaneously to achieve daily activity monitoring. In addition, we design an over-segmentation suppression mechanism with two training stages in Wi-Monitor to overcome the over-segmentation issue and enhance the activity monitoring accuracy. Intensive experiments are conducted in three different scenarios and the results demonstrate the effectiveness and practicality of Wi-Monitor for daily activity monitoring.