Robust gesture recognition based on attention-deep fast convolutional neural network and surface electromyographic signals - Lin, Chuang; Wang, Yuhao; Dai, Ming (Frontiers in Neuroscience, 2024)
The surface electromyographic (sEMG) signals reflect human motor intention and can be utilized for human-machine interfaces (HMI). Comparing to the sparse multi-channel (SMC) electrodes, the high-density (HD) electrodes have a large number of electrodes and compact space between electrodes, which can achieve more sEMG information and have the potential to achieve higher performance in myocontrol. However, when the HD electrodes grid shift or damage, it will affect gesture recognition and reduce recognition accuracy. To minimize the impact resulting from the electrodes shift and damage, we proposed an attention deep fast convolutional neural network (Attention-DFCNN) model by utilizing the temporary and spatial characteristics of high-density surface electromyography (HD-sEMG) signals. Contrary to the previous methods, which are mostly base on sEMG temporal features, the Attention-DFCNN model can improve the robustness and stability by combining the spatial and temporary features. The performance of the proposed model was compared with other classical method and deep learning methods. We used the dataset provided by The University Medical Center Göttingen. Seven able-bodied subjects and one amputee involved in this work. Each subject executed nine gestures under the electrodes shift (10mm) and damage (6 channels). As for the electrodes shift 10mm in four directions (Inwards; Onwards; Upwards; Downwards) on seven able-bodied subjects, without any pre-training, the average accuracy of Attention-DFCNN (0.942 ± 0.04) is significantly higher than LSDA (0.910 ± 0.04, p<0.01), CNN (0.920±0.05, p<0.01), TCN (0.840±0.07, p<0.01), LSTM (0.864±0.08, p<0.01), Attention-BiLSTM (0.852 ± 0.07, p<0.01), Transformer (0.903 ± 0.07, p<0.01) and Swin-Transformer (0.908 ± 0.09, p<0.01). The proposed Attention-DFCNN algorithm and the way of combining the spatial and temporary features of sEMG signals can significantly improve the recognition rate when the HD electrodes grid shift or damage during wear.