A Domain Adaptive Convolutional Neural Network for sEMG-Based Gait Phase Recognition Against to Speed Changes

Gait phase recognition based on surface electromyography (sEMG) signals provides a human–robot interface (HRI) for rehabilitation robots. However, a high-performance deep learning model requires to be calibrated on a large amount of data and frequently recalibrated for new gait speeds, which raises a burden on users. In this article, a domain adaptive convolutional neural network (DACNN) model for sEMG-based gait phase recognition against speed changes is proposed, which only needs to be pretrained on a comfortable gait speed (C.G.) and quick recalibrated on new gait speeds. Specifically, a CNN-based backbone model (BM) is first constructed and pretrained using C.G. data. Then, a domain adaptation (DA) algorithm is applied for recalibration, which allows the BM to learn speed-invariant representation with a small amount of new speed data. Four subjects participated in the experiment and were required to walk at four gait speeds, and three BMs based on AlexNet, LeNet, and fusion-data-CNN (FDCNN) were set as architectural options. Compared with the fine-tuning (FT) and non-recalibration (NoRC) strategies, the results show that the proposed method significantly outperforms both baseline methods. By recalibrating on 6-s data, the proposed method can achieve an accuracy improvement of 1.5%–6.1% compared with FT and 58%–81% compared with NoRC. The average gait phase recognition accuracies are 58.13% for AlexNet, 81.56% for LeNet, and 81.53% for FDCNN, respectively. The average mean absolute errors for gait event identification are 48, 85, and 66 ms, respectively. These results indicate that the DACNN with low recalibration burden can improve the usability of sEMG-based pattern recognition systems.