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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| Vydáno v: | 2017 Chinese Automation Congress (CAC) s. 2353 - 2356 |
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01.10.2017
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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 %. |
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| 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 |
| Author_xml | – sequence: 1 surname: Haibin Zeng fullname: Haibin Zeng organization: Dept. of Biomed. Eng., Shandong Univ., Jinan, China – sequence: 2 surname: Ke Li fullname: Ke Li email: kli@sdu.edu.cn organization: Dept. of Biomed. Eng., Shandong Univ., Jinan, China – sequence: 3 surname: Xincheng Tian fullname: Xincheng Tian organization: Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China – sequence: 4 surname: Na Wei fullname: Na Wei organization: Dept. of Geriatrics, Shandong Univ., Jinan, China – sequence: 5 surname: Rui Song fullname: Rui Song organization: Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China – sequence: 6 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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| 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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