Name
Abstract | ||
|---|---|---|
This paper presents the design, optimization and performance evaluation of artificial neural network for the efficient classification of Electromyography (EMG) signals. The EMG signals are collected for different types of volunteer hand motion which are processed to extract some predefined features as inputs to the neural network. The time and time-frequency based extracted feature sets are used to train the neural network. A back-propagation neural network with Levenberg-Marquardt training algorithm has been employed for the classification of EMG signals. The results show that the designed and optimized network able to classify single channel EMG signals with an average success rate of 88.4%. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/MUSIC.2012.46 | MUSIC |
Keywords | Field | DocType |
emg signals,average success rate,levenberg-marquardt training algorithm,neural network,single channel emg signal,emg signal,artificial neural network,different type,efficient classification,optimized network,back-propagation neural network,back propagation,neural nets,artificial neural networks,levenberg marquardt algorithm,backpropagation,time frequency analysis,feature extraction | Computer science,Communication channel,Electromyography,Speech recognition,Feature extraction,Time delay neural network,Time–frequency analysis,Artificial neural network,Backpropagation,Levenberg–Marquardt algorithm | Conference |
Citations | PageRank | References |
1 | 0.36 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Md. R. Ahsan | 1 | 4 | 2.59 |
| Muhammad I. Ibrahimy | 2 | 6 | 1.94 |
| Othman O. Khalifa | 3 | 19 | 4.55 |