Name
Title | ||
|---|---|---|
Walking gait event detection based on electromyography signals using artificial neural network. |
Abstract | ||
|---|---|---|
•Classification of stance and swing phases using electromyography (EMG) signals was proposed.•Multiple feature sets gained a higher classification accuracy than single features.•Levenberg-Marquardt (LM) trained the artificial neural network (ANN) model better than scaled conjugate gradient (SCG).•The mean absolute different of proposed model with reference data was small. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.bspc.2018.08.030 | Biomedical Signal Processing and Control |
Keywords | Field | DocType |
EMG signals,Gait event,Artificial neural network,Time domain features | Time domain,Gait,Pattern recognition,Electromyography,Heel,Exoskeleton,Artificial intelligence,Artificial neural network,Artificial neural network classifier,Mathematics,Swing | Journal |
Volume | ISSN | Citations |
47 | 1746-8094 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nurhazimah Nazmi | 1 | 0 | 0.34 |
| mohd azizi abdul rahman | 2 | 23 | 4.55 |
| Shin-ichiroh Yamamoto | 3 | 37 | 3.95 |
| A. Siti Anom | 4 | 45 | 8.59 |