Proto-CSNet: A Prototype Network Model Integrating CNN and Self-Attention for Enhanced Human Activity Recognition

With the advent of the 5.5G era, integrated sensing and communication (ISAC) technology has emerged, demonstrating its high-precision sensing capabilities across industries like wireless communication, intelligent transportation, and smart homes. Researchers are particularly exploring the use of channel state information (CSI) from WiFi signals for human activity recognition. However, current research primarily relies on complex network models to manage vast CSI data for developing accurate recognition models. It confronts two main challenges in time-sensitive applications: a limited number of samples and the need for timely solutions. In this paper, we present a prototype network model that combines CNN and self-attention to address the issues raised above, namely Proto-CSNet. First, we use time-frequency analysis and filter the data to transfer the data from the time domain to the frequency domain and eliminate noise and outliers from the samples. Then, based on the ACmix model, we modify and build a novel custom feature extraction module that retains the fused CNN's local feature capture capabilities as well as self-attention's global information processing advantage. Finally, we merge the custom modules with CNNs to form a prototype network. A real-world dataset is constructed including 11 different human activities, and the extensive experimental resutls demonstrate that Proto-CSNet outperforms existing algorithms with a model inference accuracy exceeding 95%.