A Novel Hybrid Unsupervised Domain Adaptation Method for Cross-Subject Joint Angle Estimation From Surface Electromyography

Individual physiological differences constrain the cross-user application of joint angle estimation models based on surface electromyography (sEMG) signals. Current cross-user methods for myoelectric joint angle estimation often involve the use of angle or optical sensors, which increases the training burden and costs for new users. To enable new users to perform joint angle estimation solely using sEMG sensors, this study proposes a hybrid unsupervised domain adaptation network that combines multi-order metric and adversarial mechanisms (MADAN). MADAN aims to minimize the distribution differences of sEMG signals between the source and target subjects by first aligning the domain distributions and then increasing domain confusion. The effectiveness of MADAN is validated through cross-subject wrist joint angle estimation involving 10 subjects, with an estimation frequency of 20 Hz. The results demonstrate that MADAN achieves a significantly higher average coefficient of determination (0.8688 ± 0.0307) compared to other advanced UDA cross-subject estimation methods, such as TCDA (0.6534 ± 0.234) and ADANN (0.6655 ± 0.2255). Notably, MADAN requires only 20 seconds of unlabeled samples from the target subject for training. This work is expected to alleviate the cost and training burden for new users in performing myoelectric continuous motion estimation.