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

TL;DR

This paper introduces SCADANN, a novel unsupervised domain adversarial neural network that significantly improves the stability and accuracy of electromyographic gesture recognition over multiple days without requiring recalibration.

Contribution

The paper presents SCADANN, a new unsupervised adaptation method that outperforms existing algorithms in maintaining EMG gesture recognition accuracy across days.

Findings

SCADANN outperforms state-of-the-art methods in accuracy.

It reduces the need for frequent recalibration.

It is effective across different datasets and input modalities.

Abstract

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.