Multi-Branch Attention-Based Grouped Convolution Network for Human Activity Recognition Using Inertial Sensors
Yong Li, Luping Wang, Fen Liu · Electronics · 2022
Recently, deep neural networks have become a widely used technology in the field of sensor-based human activity recognition and they have achieved good results. However, some convolutional neural networks lack further selection for the extracted features, or the networks cannot process the sensor data from different locations of the body independently and in parallel. Therefore, the accuracy of existing networks is not ideal. In particular, similar activities are easy to be confused, which limits the application of sensor-based HAR. In this paper, we propose a multi-branch neural network based on attention-based convolution. Each branch of the network consists of two layers of attention-based grouped convolution submodules. We introduce a dual attention mechanism that consists of channel attention and spatial attention to select the most important features. Sensor data collected at different positions of the human body are separated and fed into different network branches for training and testing independently. Finally, the multi-branch features are fused. We test the proposed network on three large datasets: PAMAP2, UT, and OPPORTUNITY. The experiment results show that our method outperforms the existing state-of-the-art methods.