CNN-Based Multivariate Time Series Classification for Health Monitoring in Wireless Body Area Networks

A wireless body area network (WBAN) is a crucial technology for implementing intelligent health monitoring. Traditional WBANs focus on the monitoring and classification of single physiological signals, which cannot meet the comprehensive requirements for monitoring human health and behavior. This paper proposes a local feature channel fusion convolutional neural network (CNN) model that can monitor and analyze multiple physiological signals collected by WBANs and can be used for disease identification and human activity recognition. The model employs convolution kernels to perform convolution operations on each channel, extracting local features of each channel and then performing channel fusion convolution operations to effectively integrate information between different channels. Additionally, the model incorporates an attention mechanism to dynamically adjust feature weights, highlight important features, and suppress redundant information. Experiments conducted on 11 WBAN-related datasets from the UEA database demonstrate that the proposed model achieves optimal classification performance.