Publikation: Motion intent recognition of individual fingers based on mechanomyogram
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2017
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Pattern Recognition Letters. 2017, 88, pp. 41-48. ISSN 0167-8655. eISSN 1872-7344. Available under: doi: 10.1016/j.patrec.2017.01.012
Zusammenfassung
The mechanomyogram (MMG) signals detected from forearm muscle group contain abundant information which can be utilized to predict finger motion intention. Few works have been reported in this area especially for the recognition of individual finger motions, which however is crucial for many applications such as prosthesis control. In this paper, a MMG based finger gesture recognition system is designed to identify the motions of each finger. In this system, three kinds of feature sets, wavelet packet transform (WPT) coefficients, stationary wavelet transform (SWT) coefficients, and the time and frequency domain hybrid (TFDH) features, are adopted and processed by a support vector machine (SVM) classifier. The experimental results show that the average accuracy rates of recognition using the WPT, SWT and TFDH features are 91.64%, 94.31%, and 91.56%, respectively. Furthermore, the average rate of 95.20% can be achieved when above three feature sets are combined to use in the proposed recognition system.
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DING, Huijun, Qing HE, Lei ZENG, Yongjin ZHOU, Minmin SHEN, Guo DAN, 2017. Motion intent recognition of individual fingers based on mechanomyogram. In: Pattern Recognition Letters. 2017, 88, pp. 41-48. ISSN 0167-8655. eISSN 1872-7344. Available under: doi: 10.1016/j.patrec.2017.01.012BibTex
@article{Ding2017-03Motio-38904,
year={2017},
doi={10.1016/j.patrec.2017.01.012},
title={Motion intent recognition of individual fingers based on mechanomyogram},
volume={88},
issn={0167-8655},
journal={Pattern Recognition Letters},
pages={41--48},
author={Ding, Huijun and He, Qing and Zeng, Lei and Zhou, Yongjin and Shen, Minmin and Dan, Guo}
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<dcterms:abstract xml:lang="eng">The mechanomyogram (MMG) signals detected from forearm muscle group contain abundant information which can be utilized to predict finger motion intention. Few works have been reported in this area especially for the recognition of individual finger motions, which however is crucial for many applications such as prosthesis control. In this paper, a MMG based finger gesture recognition system is designed to identify the motions of each finger. In this system, three kinds of feature sets, wavelet packet transform (WPT) coefficients, stationary wavelet transform (SWT) coefficients, and the time and frequency domain hybrid (TFDH) features, are adopted and processed by a support vector machine (SVM) classifier. The experimental results show that the average accuracy rates of recognition using the WPT, SWT and TFDH features are 91.64%, 94.31%, and 91.56%, respectively. Furthermore, the average rate of 95.20% can be achieved when above three feature sets are combined to use in the proposed recognition system.</dcterms:abstract>
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