EMG-based hand gesture classifier robust to daily variation: Recursive domain adversarial neural network with data synthesis

Abstract

ObjectiveIf a supervised classification model is used to predict hand gestures using electromyography (EMG), the EMG signals for training should be labeled every day due to their daily variations. However, annotating these signals every day is time-consuming. MethodsTo address this problem, this study proposes a new framework that updates the EMG classifier in a semi-supervised manner; the classifier was optimized to a target day by using the labeled past EMG signals and the unlabeled signals of the target day. Specifically, the following models were integrated to maximize the classification accuracy: first, a domain-adversarial neural network (DANN) was used to account for the domain shift between the EMG signals of the past and target days. Second, the past dataset was augmented by a data synthesis model incorporated with clustering, random selection, and correlation (CRC). Lastly, a recursive DANN structure was developed to augment the unlabeled EMG signals of the target day. ResultsThe performance of the proposed framework was validated with EMG data of four subjects and five different days. The classification accuracies are 58.22% (without DANN), 66.65% (with DANN), and 73.67% (with recursive DANN and CRC), respectively. ConclusionsThe recursive DANN with CRC enhances the domain adaptation effect, and thus it can address the daily variation problem of EMG. SignificanceThe proposed framework can also be extended to resolve a subject variation problem of EMG.