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Your search keyword '"Crepin, Roxane"' showing total 5 results
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5 results on '"Crepin, Roxane"'

2. Real-time hand motion recognition using sEMG Patterns Classification

Author

Crepin, Roxane, Fall, Cheikh Latyr, Mascret, Quentin, Gosselin, Clément, Campeau-Lecours, Alexandre, Gosselin, Benoit, Crepin, Roxane, Fall, Cheikh Latyr, Mascret, Quentin, Gosselin, Clément, Campeau-Lecours, Alexandre, and Gosselin, 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.

Published
2018

3. Real-time hand motion recognition using sEMG Patterns Classification

Author

Gosselin, Benoit, Gosselin, Clément, Campeau-Lecours, Alexandre, Crepin, Roxane, Fall, Cheikh Latyr, Mascret, Quentin, Gosselin, Benoit, Gosselin, Clément, Campeau-Lecours, Alexandre, Crepin, Roxane, Fall, Cheikh Latyr, and Mascret, Quentin

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.

Published
2018

5. Real-Time Hand Motion Recognition Using sEMG Patterns Classification.

Author

Crepin R, Fall CL, Mascret Q, Gosselin C, Campeau-Lecours A, and Gosselin B

Subjects
Artificial Limbs, Humans, Electromyography, Fingers physiology, Hand physiology, Movement
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 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.

Published
2018
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