Data Management for Transfer Learning Approaches to Elbow EMG-Torque Modeling.

The performance of single-use subject-specific electromyogram (EMG)-torque models degrades significantly when used on a new subject, or even the same subject on a second day. Improving the generalization performance of models is essential but challenging. In this work, we investigate how data management strategies contribute to the performance of elbow joint EMG-torque models in cross-subject evaluation. Data management can be divided into two parts, namely data acquisition and data utilization. For data acquisition, analysis of data from 65 subjects shows that training set data diversity (number of subjects) is more important than data size (total data duration). For data utilization, we propose a correlation-based data weighting (COR-W) method for model calibration which is unsupervised in the modeling stage. We first evaluated the domain shift level between data in each training trial (source domain) and data acquired from a new subject (target domain) via the mismatch of feature correlation, using only EMG signals in the target domain without the synchronized torque values (hence unsupervised during model training). Data weights were assigned to each training trial according to different domain shift levels. The weighted least squares method using the obtained data weights was then employed to develop a calibrated EMG-torque model for the new subject. The COR-W method can achieve a low root mean square error (9.29% maximum voluntary contraction) in cross-subject evaluation, with significant performance improvement compared to models without calibration. Both the data acquisition and utilization strategies contribute to the performance of EMG-torque models in cross-subject evaluation.