Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures
| Autor: | T. Jayasree, S Mary Vasanthi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Discrete wavelet transform
Support Vector Machine Computer science 0206 medical engineering 02 engineering and technology Electromyography Signal Pattern Recognition Automated Finger movement 0202 electrical engineering electronic engineering information engineering medicine Humans Time domain Gestures medicine.diagnostic_test Artificial neural network business.industry Mechanical Engineering Signal Processing Computer-Assisted Pattern recognition General Medicine Hand 020601 biomedical engineering Pattern recognition (psychology) 020201 artificial intelligence & image processing Artificial intelligence business Gesture |
| Zdroj: | Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine. 234:639-648 |
| ISSN: | 2041-3033 0954-4119 |
| DOI: | 10.1177/0954411920912119 |
| Popis: | The problem of classifying individual finger movements of one hand is focused in this article. The input electromyography signal is processed and eight time-domain features are extracted for classifying hand gestures. The classified finger movements are thumb, middle, index, little, ring, hand close, thumb index, thumb ring, thumb little and thumb middle and the hand grasps are palmar class, spherical class, hook class, cylindrical class, tip class and lateral class. Four state-of-the-art classifiers namely feed forward artificial neural network, cascaded feed forward artificial neural network, deep learning neural network and support vector machine are selected for this work to classify the finger movements and hand grasps using the extracted time-domain features. The experimental results show that the artificial neural network classifier is stabilized at 6 epochs for finger movement dataset and at 4 epochs for hand grasps dataset with low mean square error. However, the support vector machine classifier attains the maximum accuracy of 97.3077% for finger movement dataset and 98.875% for hand grasp dataset which is significantly greater than feed forward artificial neural network, cascaded feed forward artificial neural network and deep learning neural network classifiers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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