Interpretable Dual-branch EMGNet: A transfer learning-based network for inter-subject lower limb motion intention recognition

• Published in: Engineering applications of artificial intelligence Vol. 130; p. 107761
• Main Authors: Zhang, Changhe, Wang, Xiaoyun, Yu, Zidong, Wang, Bingjin, Deng, Chao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2024
• Subjects: Dual-branch network; Interpretable deep learning; Joint angle prediction; Lower limb motion recognition; Surface electromyography; Transfer learning; Joint angle prediction; Surface electromyography; Lower limb motion recognition; Transfer learning; Interpretable deep learning; Dual-branch network
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.107761

Currently, the fusion of surface Electromyography (EMG) and deep learning is gradually showing immense potential in the research of Lower Limb Motion Intention Recognition (LLMIR). Nevertheless, most deep learning algorithms have poor interpretability without special design or the help of other post-hoc analysis tools, as well as unsatisfactory performance in cross-subject prediction. Hence, this paper presents a novel Interpretable Dual-Branch EMG Network (IDB-EMGNet), in which one branch is dedicated to lower limb motion recognition, and the other is able to predict knee joint angles in advance. The shallow feature extraction module of IDB-EMGNet is constructed using an ante-hoc interpretable SincNet technique, which enables the detection of the spectral range of EMG used for the LLMIR task. An improved bottleneck block with shuffle attention is designed for deep feature extraction, which enhances model performance with only a little increase in complexity. The performance of IDB-EMGNet in both intra-subject and inter-subject scenarios is investigated, where the latter integrates the transfer learning technique. Specifically, by conducting model pre-training on source-domain subjects and transferring the learned knowledge to target-domain subjects, satisfactory performance can be achieved even with less computing resource. Experimental results on two publicly available datasets indicate that the proposed approach exhibits superior applicability to both normal subjects and knee-pathology patients, showing a promising prospect in the controller design of human-robot collaborative exoskeletons. •A novel IDB-EMGNet is proposed for simultaneous discrete and continuous lower limb motion intention recognition.•Interpretability of IDB-EMGNet is enhanced via an ante-hoc SincConv technique.•An IB-Neck block is designed to improve model prediction performance.•Transfer learning technique is utilized to improve model generalization in inter-subject prediction.•The proposed approach performs well for both normal subjects and knee-pathology patients.