Real-time hand motion recognition using sEMG Patterns Classification

Authors: Crepin, RoxaneFall, Cheikh LatyrMascret, QuentinGosselin, ClémentCampeau-Lecours, AlexandreGosselin, Benoit
Abstract: Increasing performance while decreasing the cost of sEMG prostheses is an important milestone in rehabilitation engineering. The different types of prosthetic hands that are currently available to patients worldwide can benefit from more effective and intuitive control. This paper presents a real -time approach to classify finger motions based on surface electromyography (sEMG) signals. A multichannel signal acquisition platform implemented using components of f the shelf is use d to record forearm sEMG signals from 7 channels. sEMG pattern classification is performed in real time, using a Linear Discriminant Analysis approach. Thirteen hand motions can be successfully identified with an accuracy of up to 95. 8% and of 92. 7% on average for 8 participants, with an updated prediction every 192 ms.
Document Type: Actes de conférence
Issue Date: 1 August 2018
Open Access Date: Restricted access
Document version: VoR
This document was published in: International Conference of the IEEE Engineering in Medicine and Biology Society
IEEE Engineering in Medicine and Biology Society
Collection:Autres articles publiés

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