Multi-User Human Activity Recognition Through Adaptive Location-Independent WiFi Signal Characteristics

In recent years, the remarkable advancement of WiFi sensing technologies has opened new frontiers in human activity recognition, enabling innovative solutions that transcend traditional methods and improve the capabilities of intelligent environments. Individual dynamic movements such as walking, sitting, standing, and running, as well as more complex interactions such as sports activities, are all examples of human activity. WiFi sensing has emerged as a powerful tool for human activity recognition; however, certain restrictions persist, especially when sensing activities involving multiple users across different locations. These limitations highlight the need for innovative techniques to address the intricacies of multi-user scenarios and environmental effects, ensuring the robustness and accuracy of WiFi-based sensing systems. To address multi-user effects in WiFi signals, we propose a few layering LSTM deep learning models with Raspberry Pi for edge computing solutions. The method leverages the decomposition of Channel State Information (CSI) signals through Independent Component Analysis (ICA) and Continuous Wavelet Transform (CWT). The integration of signal decomposition and deep learning holds promise for advancing WiFi sensing systems’ accuracy, reliability, and real-time capabilities in complex environments and multi-user scenarios. Experimental findings prove the system’s ability to handle complex activities with high classification accuracy. Furthermore, the system displays a remarkable ability to classify complex activities. By leveraging the power of deep learning, the model learns intricate patterns and relationships within the decomposed CSI signals, enabling it to distinguish between diverse activities with high accuracy.