EMG Pattern Recognition Using Decomposition Techniques for Constructing Multiclass Classifiers

To improve the dexterity of multi-functional myoelectric prosthetic hand, more accurate hand gesture recognition based on surface electromyographic (sEMG) signal is needed. This paper evaluates two types of time-domain EMG features, one independent feature and one combined feature including four features. The selected features from eight subjects with 13 finger movements were tested with four decomposed multi-class support vector machines (SVM), four decomposed linear discriminant analyses (LDA) and a multi-class LDA. The classification accuracy, training, and classification time are compared. The results have shown that the combined features decrease error rate, and binary tree based decomposition multi-class classifiers yield the highest classification success rate (88.2%) with relatively low training and classification time.


Published in:
2016 6Th Ieee International Conference On Biomedical Robotics And Biomechatronics (Biorob), 1296-1301
Presented at:
[u'6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)', u'6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)']
Year:
2016
Publisher:
New York, Ieee
ISSN:
2155-1782
ISBN:
978-1-5090-3287-7
Laboratories:




 Record created 2017-02-17, last modified 2018-03-17


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