CSI-based location-independent Human Activity Recognition with parallel convolutional networks

Human Activity Recognition (HAR) based on Wi-Fi has a broad application prospect in human–computer interaction. Since Wi-Fi signals are sensitive to the environmental changes, the features of the same category of human activity at different locations have significant difference. The existing HAR systems based on Wi-Fi need to re-collect samples or retrain models when recognizing the same activity at new locations, which reduces their practicability in human–computer interaction. To address this challenge, this paper proposes a CSI-based Parallel Convolutional Networks-based location-independent HAR system (CSI-PCNH). CSI-PCNH enhances the inter-class difference by extracting the inter-class features of the different activity samples. In addition, CSI-PCNH improves the generalization ability of activity recognition at any location by extracting the intra-class features of the same category of activity at different locations. In order to obtain the inter-class features and intra-class features of activity samples, we design a parallel convolutional network model which is composed of 3DCNN combined with Channel Attention Mechanism (CAM) and 2DCNN with LSTM to extract the global and local spatial–temporal features of the activity samples. The experimental results show that in the 8 m × 7 m indoor area, the proposed HAR system trained by the activity samples at 12 known locations, the average recognition accuracy for 6 categories of activities at any other 10 locations can reach 91.7%.