| publication name | Evaluating the Performance of Neural Network and Kalman Filter Based Linear Model on Classification of Hand EMG Signals |
|---|---|
| Authors | Abdullah A.; M. Magdy; A. El-Assal; A. El-Betar; Hussein F. M. Ali |
| year | 2019 |
| keywords | Electromyographic signal, Neural network, Autoregressive, Kalman filter, Pattern recognition, Classification |
| journal | 7th International Japan-Africa Conference on Electronics, Communications, and Computations,(JAC-ECC) (pp. 76-79). IEEE. |
| volume | Not Available |
| issue | Not Available |
| pages | Not Available |
| publisher | Not Available |
| Local/International | International |
| Paper Link | https://ieeexplore.ieee.org/abstract/document/9051106 |
| Full paper | download |
| Supplementary materials | Not Available |
Abstract
In recent years, many revolutionary algorithms were designed for enhancing the performance of the neural network classification. This paper aims at evaluating the efficiency of one of these algorithms in intuitive control of the prosthetic hands. We used a combination of a neural network and a Kalman filter based linear model for the classification of 4 movement patterns by recruiting a single electromyographic channel electrode. The resultant recognition accuracy reached 95.4% with a mean squared error of 0.0473. The results show that the proposed technique is promising and competitive compared to traditional classification strategies.