LiteWiHAR: A Lightweight WiFi-based Human Activity Recognition System

By analyzing changes in Channel State Information (CSI) during the propagation of Wi-Fi signals in indoor environ-ments, researchers can achieve contactless perception of human behaviors. Due to the high complexity of the deep models used in CSI-based human activity recognition systems, it's difficult to be employed in edge computing devices. To improve the Wi-Fi perception accuracy and reduce the system complexity, this paper proposes a lightweight human behavior perception system LiteWiHAR. The system converts the filtered CSI signal in time domain into a two-dimensional image to increase its spatial structure information. Using ConvNeXt v2 layered encoder as the backbone feature extraction network, perceptual information containing deep and shallow features in the image is extracted layer by layer. By reducing the number of depthwise separable convolution channels and compressing the number of stacked blocks in the encoder, the degree of network fragmentation is reduced and the number of model parameters is compressed. Finally, the SimAM parameterless attention module is introduced to assist the network in capturing global information. The system is lightweight while maintaining good feature extraction capability. We validated the effectiveness of LiteWiHAR on three open-source datasets and demonstrated its superiority over other state-of-the-art systems.