Name
Abstract | ||
|---|---|---|
With complex functions, hand is an important organ for human. Unfortunately, many people in China are suffering from hand losing. Therefore, the effective hand motion recognition system is required to help the amputees live or work normally. The surface electromyography (sEMG) signal can represent the hand motion effectively, and many studies about sEMG-based prosthetic hands have been investigated. However, some prosthetic hands use on-off switch control command, which limits the intelligence and flexibility of the prosthetic hands. Some intelligent recognition systems require too many sensors, which is unrealistic for amputees with limited residual muscles. In addition, some algorithms are too complicated, which brings difficulties for practical applications. To solve these problems, we attempted to recognize six commonly used hand gestures with two-channel sensors, and the classification performance and calculation time of different algorithms are compared. Finally, we achieved the recognition accuracy of 91.93% by three time domain features and back propagation neural network (BPNN) classifier, which balances the accuracy and computation time. In future work, the proposed method will be applied to real-time prosthetic hands to improve the amputee's quality of life. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICARM.2019.8834153 | 2019 IEEE 4TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2019) |
Keywords | Field | DocType |
surface electromyography (sEMG), time domain analysis, back propagation neural network (BPNN), support vector machines (SVM) | Time domain,Residual,Computer vision,Recognition system,Motion recognition,Gesture,Computer science,Back propagation neural network,Artificial intelligence,Classifier (linguistics),Computation | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yikang Yang | 1 | 0 | 0.34 |
| Feng Duan | 2 | 87 | 27.49 |
| Jia Ren | 3 | 0 | 0.34 |
| Zhenqiang Liu | 4 | 0 | 0.34 |
| Chi Zhu | 5 | 15 | 6.00 |
| Yew Guan Soo | 6 | 0 | 0.34 |
| Kyung-Ryoul Mun | 7 | 0 | 0.68 |