Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition

• Published in: IEEE access Vol. 8; pp. 177941 - 177955
• Main Authors: Cote-Allard, Ulysse, Gagnon-Turcotte, Gabriel, Phinyomark, Angkoon, Glette, Kyrre, Scheme, Erik J., Laviolette, Francois, Gosselin, Benoit
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Calibration; Clustering algorithms; Computer architecture; Control methods; Datasets; domain adaptation; domain adversarial; Domains; Electrodes; Electromyography; EMG; Gesture recognition; Heuristic algorithms; Machine learning; Myoelectric control; Myoelectricity; Neural networks; Real-time systems; Recording; Self calibration; Spectrograms; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3027497

Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.