Synergy-Based Multi-fingers Forces Reconstruction and Discrimination from Forearm EMG

Abstract

In this paper we propose a novel synergy-based myocontrol scheme for finger force estimation and classification which is able to simultaneously control 4 fingers with a training phase based only on individual-finger data. The proposed method has been tested using the online-available NinaPro database and validated in a preliminary experiment conducted with the use of a hand-exoskeleton. Results show how the presented approach outperforms considerably the linear regression method which is considered standard approach in myoelectric control. The low error rate obtained (smaller than 10% of the targeted force) and the effectiveness in decreasing the number of false activation open the possibilities for future uses in fields such as haptics and neuro-rehabilitation.