Human Activity Recognition Based on Self-Attention Mechanism in WiFi Environment

Abstract

In recent years, the use of WiFi Channel State Information (CSI) for Human Activity Recognition (HAR) has attracted widespread attention, thanks to its low cost and non-intrusive advantages. Previous research mostly used models based on Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN) for activity recognition. However, these methods fail to achieve good parallelism while learning global features and fine-grained features, so they often cannot achieve the ideal recognition effect or training speed. In light of this, we propose an ensemble deep learning model based on CNN and Transformer, ConTransEn. Specifically, we first use CNN to extract spatial features of the sequence, and then use Vision Transformer (ViT) to further extract temporal features. The Transformer introduces self-attention mechanism, enabling the model to fully consider information from other positions in the sequence, rather than being limited to the current input. Furthermore, due to the increased parallelism, Transformer has an advantage in training speed over RNN. In order to further improve the accuracy and robustness of the model, we adopt a bagging ensemble learning strategy, integrating the prediction results of multiple homogeneous base models using a soft voting method to obtain the final classification result. This ensemble learning method reduces the risk of model overfitting, and improves the overall accuracy and reliability of the model. We extensively evaluated the model on two publicly available datasets, and achieved excellent recognition results, indicating its good performance and robustness. The average recognition accuracy on the UT-HAR dataset reached 99.41%, surpassing existing solutions.

Keywords

• Attention
• channel state information (CSI)
• convolutional neural networks
• human activity recognition