WIFI based human activity recognition using multi-head adaptive attention mechanism

Abstract

Human Activity Recognition (HAR) utilizing Channel State Information (CSI) extracted from WiFi signals has garnered substantial interest across various domains and applications. This field’s potential paths and applications extend beyond CSI-based HAR and include smart homes, assisted living, security, gaming, surveillance, and context-aware computing. The ability of deep learning algorithms to effectively process and interpret CSI data opens up new possibilities for accurate and robust human activity recognition in real-world scenarios. However, traditional Recurrent Neural Networks (RNN) models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), rely solely on their internal memory cells to maintain information over time. Important details might be diluted or lost within the memory cells in complex CSI sequences. To address this limitation, we propose a lightweight approach that incorporates a multi-head adaptive attention weight mechanism MHAAM into the HAR framework. The multi-head attention mechanism allows the model to attend to different informative patterns within the CSI data simultaneously, capturing fine-grained temporal dependencies and improving the model’s ability to recognize complex activities. The implemented models effectively filter out noise and irrelevant information by assigning higher weights to informative CSI features, further enhancing activity classification accuracy. Experimental evaluations and comparative analyses of HAR for seven activities demonstrate that attention-based RNN models with multi-head attention consistently outperform traditional RNN models. The multi-head attention mechanism achieves improved generalization and testing for seven common human activities and environments, leading to a higher complex human activity classification accuracy of up to 98.5%.