LAUSR

Continuous estimation of finger joints based on surface electromyography (sEMG) has attracted much attention in the field of human-machine interface (HMI). A couple of deep learning models were proposed to estimate the finger joint angles for specific subject. When applied onto a new subject, however, the performance of the subject-specific model would degrade significantly due to the inter-subject differences. Therefore, a novel cross-subject generic (CSG) model was proposed in this study to estimate continuous kinematics of finger joints for new users. Firstly, a multi-subject model based on the LSTA-Conv network was built by using sEMG and finger joint angles data from multiple subjects. Then, the subjects adversarial knowledge (SAK) transfer learning strategy was adopted to calibrate the multi-subject model with the training data from a new user. With the updated model parameters and the testing data from the new user, multiple finger joint angles could be estimated afterwards. The overall performance of the CSG model for new users was validated on three public datasets from Ninapro. The results showed that the newly proposed CSG model significantly outperformed five subject-specific models and two transfer learning models in terms of Pearson correlation coefficient, root mean square error, and coefficient of determination. Comparison analysis showed that both the long short-term feature aggregation (LSTA) module and the SAK transfer learning strategy contributed to the CSG model. Moreover, increasing number of subjects in training set improved the generalization capability of the CSG model. The novel CSG model would facilitate the application of robotic hand control and other HMI settings.