Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8512820 | EMBC |
Field | DocType | Volume |
Computer vision,Off the shelf,Thumb,Motion recognition,Computer science,Electromyography,Rehabilitation engineering,Artificial intelligence,Linear discriminant analysis,Statistical classification,Classifier (linguistics) | Conference | 2018 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roxane Crepin | 1 | 0 | 0.34 |
| Cheikh Latyr Fall | 2 | 3 | 2.83 |
| Quentin Mascret | 3 | 0 | 0.34 |
| Clément Gosselin | 4 | 484 | 66.28 |
| Alexandre Campeau-Lecours | 5 | 16 | 7.49 |
| B Gosselin | 6 | 353 | 60.22 |