  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.
