Wiga: A WiFi-Based Contactless Activity Sequence Recognition System Based on Deep Learning

Monitoring aperiodic activity sequence contributes a lot to home exercise guidance and sports experience but existing approaches are designed for quasi-period activity or isolated activity monitoring. There is a compelling need for contactless real-time auxiliary exercise system, especially for aperiodic activity sequence. In this paper, we present Wiga, a WiFi-based real-time contactless activity sequence recognition system, which can recognize activity sequences even for users who have not participated in the training phase. Wiga takes the fine-grained Channel State Information (CSI) as input and elaborates a deep learning network to map the motion-induced signal variations with the activity sequence. First, Wiga removes noise and redundancy of the raw CSI measurements. Then, after abstracting deep features with a Convolutional Neural Network (CNN), Wiga exploits a Long Short Term Memory (LSTM) network to model temporal dependencies of the sequence. In addition, Wiga employs the beam search method to get around error-prone temporal segments and obtains real-time activity sequence recognition. We evaluate Wiga with 17 yoga activities from 7 volunteers, and extensive experimental results show that Wiga achieves an average accuracy of 97.7% and 85.6% for trained and untrained users respectively with a recognition delay no more than 0.5s.