Real-Time Hand Motion Recognition Using sEMG Patterns Classification

Roxane Crepin • Simon Tam • Fall Cheikh Latyr • Quentin Mascret • Clément Gosselin • Alexandre Campeau-Lecours • Benoit Gosselin

08:30 - 10:00 | Monday 29 October 2018 | Ambassador C



Increasing performance while decreasing the cost of sEMG prostheses is an important milestone in rehabilitation engineering. 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 off the shelf is used 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.