Multi-Variations Activity Based Gaits Recognition Using Commodity WiFi

With the prevalence of commercial WiFi devices and the development of Internet of Things (IoT), researchers have extended the usage of WiFi from communication to sensing. Recently, device-free human activity recognition has been applied to support WiFi based remote control and human-computer interaction. However, prior works usually recognize each individual activity by extracting the feature of corresponding WiFi signals, and hence distinguishing differences between human activities. Once the activity has multiple variations of different body parts (such as head, arm and leg), distinguishing such sub-activities is extremely difficult. In this paper, we propose a Multi-variations Activity Recognition (MAR) system to identify multiple-variations of body parts. Our work is based on an observation that the Channel State Information (CSI) is sensitive to the activities of body parts. Firstly, CANDECOMP/ PARAFAC (CP) decomposition and Dynamic Time Warping (DTW) are applied to recognize multi-variations activities. Secondly, we theoretically analyse the uniqueness of CP decomposition. Then, we design specific experiment to verify the reliability and stability of uniqueness. Finally, we apply MAR in gaits recognition of multiple volunteers to evaluate the accuracy performance. The experiment results demonstrate that MAR achieves average 95% accuracy in gaits recognition.