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Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal

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Abstract

Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested.

Mean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier

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Acknowledgements

The authors would like to thank the Research and Development Office (RDO), Prince of Songkla University, and Associate Professor Dr. Seppo Karrila, Faculty of Science and Industrial Technology, Prince of Songkla University, for commenting on the manuscript.

Funding

This work was jointly funded by the Thailand Research Fund and Faculty of Engineering, Prince of Songkla University, through Contract No. RSA5980049, in part by the Higher Education Research Promotion and National Research University Project of Thailand, Office of the Higher Education Commission, and UTS International Research Scholarship, University of Technology, Sydney.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, 90112, Thailand

    Pornchai Phukpattaranont, Sirinee Thongpanja & Chusak Limsakul

  2. School of Electrical, Mechanical and Mechatronic Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway, Ultimo, NSW, 2007, Australia

    Khairul Anam & Adel Al-Jumaily

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  1. Pornchai Phukpattaranont
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Correspondence to Pornchai Phukpattaranont.

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Phukpattaranont, P., Thongpanja, S., Anam, K. et al. Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal. Med Biol Eng Comput 56, 2259–2271 (2018). https://doi.org/10.1007/s11517-018-1857-5

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