Robust Human Activity Recognition System with Wi-Fi Using Handcraft Feature

WiFi-based Human activity recognition (HAR) system has the drawback of the new domain inadaptability. Numerous studies have proposed to solve this problem, but these methods have the limitations of needing the new domain data or fine-tuning the model. In this paper, we propose HARW, a cross-domain HAR system using Wi-Fi. Specifically, a novel domain-independent feature extraction algorithm is proposed based on the multiple signal classification algorithm, which extracts three physical factors (i.e. time of flight, change rate of path length, and angle of arrival) simultaneously to construct the TCA feature. Then, A two-stage model is proposed to recognize activities based on TCA. The experimental results show that HARW can increase the average accuracy rate by 9% and the best accuracy can reach 60%, without new domain data and fine-tuning the model, outperforming the method that only uses CSI raw data. In addition, HARW adopts only a pair of Wi-Fi devices.