Name
Title | ||
|---|---|---|
Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications. |
Abstract | ||
|---|---|---|
Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11517-016-1551-4 | Med. Biol. Engineering and Computing |
Keywords | Field | DocType |
Ankle joint movements,EMG,Pattern recognition,Rehabilitation,Signal processing | Signal processing,Dimensionality reduction,Detection theory,Feature (machine learning),Artificial intelligence,Classifier (linguistics),Computer vision,Naive Bayes classifier,Pattern recognition,Speech recognition,Feature extraction,Linear discriminant analysis,Mathematics | Journal |
Volume | Issue | ISSN |
55 | 5 | 1741-0444 |
Citations | PageRank | References |
3 | 0.42 | 12 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maged S. AL-Quraishi | 1 | 3 | 0.42 |
| Asnor J. Ishak | 2 | 18 | 4.24 |
| A. Siti Anom | 3 | 45 | 8.59 |
| Mohd K. Hasan | 4 | 3 | 0.42 |
| Muhammad Al-Qurishi | 5 | 73 | 10.17 |
| Hossein Ghapanchizadeh | 6 | 3 | 0.42 |
| atif alamri | 7 | 1108 | 69.29 |