Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition

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 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 ranked against three state-of-the-art domain adversarial algorithms and two state-of-the-art self-calibrating algorithms developed specifically for EMG-based deep network adaptation on both offline and dynamic datasets. Overall, SCADANN is shown to systematically improve classifiers' performance over no adaptation and ranks first on almost all the tested cases.