Classification of gait phases based on a machine learning approach using muscle synergy
The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular or...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2023.1201935/full |
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| author | Heesu Park Heesu Park Sungmin Han Sungmin Han Joohwan Sung Soree Hwang Soree Hwang Inchan Youn Inchan Youn Seung-Jong Kim |
| author_facet | Heesu Park Heesu Park Sungmin Han Sungmin Han Joohwan Sung Soree Hwang Soree Hwang Inchan Youn Inchan Youn Seung-Jong Kim |
| author_sort | Heesu Park |
| collection | DOAJ |
| description | The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection. |
| first_indexed | 2024-03-13T10:54:30Z |
| format | Article |
| id | doaj.art-8dd704814c884fe39c57d49b181f51c5 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-03-13T10:54:30Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-8dd704814c884fe39c57d49b181f51c52023-05-17T05:42:31ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612023-05-011710.3389/fnhum.2023.12019351201935Classification of gait phases based on a machine learning approach using muscle synergyHeesu Park0Heesu Park1Sungmin Han2Sungmin Han3Joohwan Sung4Soree Hwang5Soree Hwang6Inchan Youn7Inchan Youn8Seung-Jong Kim9Biomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of KoreaDepartment of Biomedical Engineering, Korea University College of Medicine, Seoul, Republic of KoreaBiomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of KoreaDivision of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology, Seoul, Republic of KoreaBiomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of KoreaBiomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of KoreaSchool of Biomedical Engineering, Korea University, Seoul, Republic of KoreaBiomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of KoreaDivision of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology, Seoul, Republic of KoreaDepartment of Biomedical Engineering, Korea University College of Medicine, Seoul, Republic of KoreaThe accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.https://www.frontiersin.org/articles/10.3389/fnhum.2023.1201935/fullmuscle synergyneurologicalmuscle modulegait phaseelectromyography (EMG) |
| spellingShingle | Heesu Park Heesu Park Sungmin Han Sungmin Han Joohwan Sung Soree Hwang Soree Hwang Inchan Youn Inchan Youn Seung-Jong Kim Classification of gait phases based on a machine learning approach using muscle synergy Frontiers in Human Neuroscience muscle synergy neurological muscle module gait phase electromyography (EMG) |
| title | Classification of gait phases based on a machine learning approach using muscle synergy |
| title_full | Classification of gait phases based on a machine learning approach using muscle synergy |
| title_fullStr | Classification of gait phases based on a machine learning approach using muscle synergy |
| title_full_unstemmed | Classification of gait phases based on a machine learning approach using muscle synergy |
| title_short | Classification of gait phases based on a machine learning approach using muscle synergy |
| title_sort | classification of gait phases based on a machine learning approach using muscle synergy |
| topic | muscle synergy neurological muscle module gait phase electromyography (EMG) |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2023.1201935/full |
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