Wi-Exercise: An Indoor Human Movement Detection Method Based on Bidirectional LSTM Attention

Abstract

Human motion recognition has become a hot topic in the field of human-computer interaction. Due to the traditional method of detecting human movement in indoor environments, there are still problems such as high hardware costs, influenced by the environment and the need for experimenters to be equipped with relevant equipment. With the large-scale deployment of wireless devices and the establishment of wireless network infrastructure, indoor personnel movement recognition systems based on wireless signals are gradually proposed. In this paper, we propose a device-independent motion-detection method Wi-exercise based on temporal recurrent neural networks. The acquired channel state information (CSI) is preprocessed by wavelet transform, and the output CSI matrix is filtered by the principal component analysis (PCA) to extract the feature values. The wavelet function is processed to further remove the noise also to make the action data more distinctive, and then PCA is used to track the entire CSI time series to improve and maintain its dimensional energy. The preprocessed data are subjected to feature extraction to obtain CSI of human motion and establish feature sequences, and then the action model is trained using the bidirectional long short-term memory attention (ABLSTM) mechanism to finally realize human motion recognition. Finally, the robustness of Wi-exercise is tested experimentally, and its performance is compared with that of existing recognition methods. The experimental results show that the average recognition rate of the method is 94.43% in the complex indoor environment, and the method is better than the traditional classifier method for action recognition, with lower and higher recognition accuracy.