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

摘要

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 x 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%.

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