Wearable sensors for human activity recognition based on a self-attention CNN-BiLSTM model

Abstract

Purpose This study aims to reduce data bias during human activity and increase the accuracy of activity recognition. Design/methodology/approach A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers. Findings The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems. Originality/value Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.