Name
Title | ||
|---|---|---|
Continuous estimation of upper limb joint angle from sEMG signals based on SCA-LSTM deep learning approach. |
Abstract | ||
|---|---|---|
•A deep learning neural network named short-connected autoencoder long short term memory based is proposed, and successfully solved the problem in simultaneous and proportional robotic arm control.•The work specifically built a model implied the inner relationship map between the surface electromyographic signals and the joint angles of shoulder and elbow.•The proposed estimation method only requires 5 channels electromyography signal input but provides 2 channels joint angle signals on shoulder and 1 channel joint angle signals on elbow.•The average correlation coefficient of the estimated joint angle signals and the real joint angle signals reaches 95.7%. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.bspc.2020.102024 | Biomedical Signal Processing and Control |
Keywords | DocType | Volume |
Robotic arm control,Surface electromyogram,Simultaneous and proportional control,Joint angle estimation,Deep learning | Journal | 61 |
ISSN | Citations | PageRank |
1746-8094 | 3 | 0.40 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenfei Ma | 1 | 3 | 1.07 |
| Chuang Lin | 2 | 3040 | 390.74 |
| Guanglin Li | 3 | 314 | 57.23 |
| Guanglin Li | 4 | 314 | 57.23 |
| Lisheng Xu | 5 | 178 | 39.09 |