A Location-Independent Human Activity Recognition Method Based on CSI: System, Architecture, Implementation

In the application of human activity recognition (HAR) based on channel state information (CSI), due to the high dynamic characteristics of wireless channel to different environments, the features of human activity samples in different locations are different. In addition, the existing CSI-based HAR approaches limit the extraction of activity features to the euclidean space and ignores the rich relational information between samples, categories and locations, which result in insufficient generalization performance for location-independent HAR. To address this challenge, this paper proposes a CSI-based location-independent HAR system CSI-MTGN. The system represents the classification task under each training sample collection location (TSCL) as a task, which is composed of three interactive parts: sample hidden representation, activity features extraction based on hierarchical graph neural network (HGNN) and information exchange based on multi-task learning. The proposed system improves the sample hidden representation, which is benefit for activity feature extraction and classification. The HGNN is designed to express various relationship information between samples, categories and locations in the form of graph structure, and the classification task under each TSCL is constructed through data augmentation, so as to improve the knowledge understanding and inference capabilities of the recognition model. The multi-task learning is used to achieve implicit data augmentation by sharing parameters among tasks through soft parameter sharing, and improves the generalization performance of the system. To validate the performance of the proposed system, experiments were conducted in a hall and a conference room, where samples of 10 categories of activities under 7 TSCLs were used for training the system, and the HAR accuracy rates at any locations were 94.1% and 93.3%, respectively.