Unsupervised domain adversarial self-calibration for electromyography-based gesture recognition

Type de document

Études primaires

Année de publication

2020

Langue

Anglais

Titre de la revue

IEEE Access

Première page

177941

Dernière page

177955

Résumé

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. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Mots-clés

Électromyographie, Electromyography

Numéro de projet IRSST

n/a

Ce document n'est pas disponible pour le moment.

Partager

COinS