Toward Highly Flexible Inter-User Calibration of Myoelectric Control Models With User-Defined Hand Gestures

Myoelectric control models enabling accurate hand gesture recognition via electromyography (EMG) have attracted increasing attentions in rehabilitation robotics. Adapting pre-trained models to new users is a main challenge in real world applications due to the inter-user different EMG characteristics. Most previous transfer learning approaches employed a rigid model calibration process, usually in a supervised manner with ground truth labels, or in an unsupervised manner but still requiring users to perform pre-defined hand gestures to update model parameters. We argue that such a rigid model calibration process lacks flexibility and limit the translation of myoelectric control into real world practice. In this work, we gradually “flexibilize” the standard model calibration process toward a highly flexible version, which does not require the labels of calibration data, and can be performed on only a subset of pre-defined hand gestures or even unknown user-defined hand gestures. We identify those key components contributing to the performance difference along the way. Compared with the supervised method, the unsupervised model calibration even contributed to a 10% improvement ( ${p}\lt 0.05$ ) in case where only a subset of gesture categories were available for model calibration. Moreover, the unsupervised model calibration achieved a highest recognition accuracy of 86.57% using unknown user-defined gestures, with no significant difference compared to the accuracy with pre-defined gestures ( ${p}\gt 0.05$ ).