Classification of hand motions using linear discriminant analysis and support vector machine

In this study, we aimed to recognize six hand motions using surface electromyogram (sEMG) signals recorded from eight muscles of the right hand. Twenty-five healthy subjects participated in this experiment. Data were segmented using windows of 250-ms length with a 150-ms overlapping. In this paper,...

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Published in:2017 Chinese Automation Congress (CAC) pp. 2353 - 2356
Main Authors: Haibin Zeng, Ke Li, Xincheng Tian, Na Wei, Rui Song, Lelai Zhou
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2017
Subjects:
classification
discriminant analysis
Electromyography
Feature extraction
Field-flow fractionation
hand motions
linear discriminant analysis
multiple regression analysis
Muscles
support vector machine
Support vector machines
surface electromyogram
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Abstract In this study, we aimed to recognize six hand motions using surface electromyogram (sEMG) signals recorded from eight muscles of the right hand. Twenty-five healthy subjects participated in this experiment. Data were segmented using windows of 250-ms length with a 150-ms overlapping. In this paper, we extracted 24 features per muscle. Three feature sets-the original features, the features produced by a discriminant analysis (DA) and those selected by a multiple regression analysis (MRA) entered into one of the following classifiers: linear discriminant analysis (LDA) or support vector machine (SVM). The results showed that the original features classified by the SVM reached an average accuracy of 91.2 ± 0.383 %, significantly higher than the other approaches. The index finger extension (IFE) had higher classification accuracy than the other hand motions. The probability of the thumb opposition (TO) falsely classified as key pinch (KP) was 1.1 %, that of the hand grasp (HG) falsely classified as four fingers flexion (FFF) was 1.0 %.
AbstractList In this study, we aimed to recognize six hand motions using surface electromyogram (sEMG) signals recorded from eight muscles of the right hand. Twenty-five healthy subjects participated in this experiment. Data were segmented using windows of 250-ms length with a 150-ms overlapping. In this paper, we extracted 24 features per muscle. Three feature sets-the original features, the features produced by a discriminant analysis (DA) and those selected by a multiple regression analysis (MRA) entered into one of the following classifiers: linear discriminant analysis (LDA) or support vector machine (SVM). The results showed that the original features classified by the SVM reached an average accuracy of 91.2 ± 0.383 %, significantly higher than the other approaches. The index finger extension (IFE) had higher classification accuracy than the other hand motions. The probability of the thumb opposition (TO) falsely classified as key pinch (KP) was 1.1 %, that of the hand grasp (HG) falsely classified as four fingers flexion (FFF) was 1.0 %.
Author Lelai Zhou
Ke Li
Haibin Zeng
Na Wei
Xincheng Tian
Rui Song
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  email: kli@sdu.edu.cn
  organization: Dept. of Biomed. Eng., Shandong Univ., Jinan, China
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  surname: Xincheng Tian
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  organization: Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China
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  surname: Na Wei
  fullname: Na Wei
  organization: Dept. of Geriatrics, Shandong Univ., Jinan, China
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  surname: Rui Song
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  organization: Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China
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  surname: Lelai Zhou
  fullname: Lelai Zhou
  organization: Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China
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Snippet In this study, we aimed to recognize six hand motions using surface electromyogram (sEMG) signals recorded from eight muscles of the right hand. Twenty-five...
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StartPage 2353
SubjectTerms classification
discriminant analysis
Electromyography
Feature extraction
Field-flow fractionation
hand motions
linear discriminant analysis
multiple regression analysis
Muscles
support vector machine
Support vector machines
surface electromyogram
Thumb
Title Classification of hand motions using linear discriminant analysis and support vector machine
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