Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG
Subjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of impairment-level recognition in several studies. This study continues our previous work on the use of EMG to recognize finger movement impairment levels. In contrast to our previous work, t...
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2024-11-01
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| author | Adhe Rahmatullah Sugiharto Suwito P Ayumi Ohnishi Yudith Dian Prawitri Riries Rulaningtyas Tsutomu Terada Masahiko Tsukamoto |
| author_facet | Adhe Rahmatullah Sugiharto Suwito P Ayumi Ohnishi Yudith Dian Prawitri Riries Rulaningtyas Tsutomu Terada Masahiko Tsukamoto |
| author_sort | Adhe Rahmatullah Sugiharto Suwito P |
| collection | DOAJ |
| description | Subjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of impairment-level recognition in several studies. This study continues our previous work on the use of EMG to recognize finger movement impairment levels. In contrast to our previous work, this study provided a better and more reliable recognition result with improved experimental settings, such as an increased sampling frequency, EMG channels, and extensive patient data. This study employed two data processing mechanisms, inter-subject cross-validation (ISCV) and data-scaled inter-subject cross-validation (DS-ISCV), resulting in two evaluation methods. The machine learning algorithms employed in this study were SVM, random forest (RF), and multi-layer perceptron (MLP). MLP_ISCV achieved the highest average recall score of 0.73 across impairment levels in the spherical grasp task. Subsequently, the highest average recall score of 0.72 among non-majority classes was achieved by SVM_DS-ISCV in the mass extension task. The cross-validation result shows that the proposed method effectively handled the imbalanced dataset without being biased toward the majority class. The proposed method demonstrated the potential to assist doctors in clarifying the subjective assessment of finger movement impairment levels. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-cac5e2e5dbdf4b1da9fc0f3d2ededb102024-12-13T16:21:52ZengMDPI AGApplied Sciences2076-34172024-11-0114231083010.3390/app142310830Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMGAdhe Rahmatullah Sugiharto Suwito P0Ayumi Ohnishi1Yudith Dian Prawitri2Riries Rulaningtyas3Tsutomu Terada4Masahiko Tsukamoto5Graduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe 657-8501, JapanGraduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe 657-8501, JapanDepartment of Physical Medicine and Rehabilitation, Faculty of Medicine, Airlangga University, Airlangga University Hospital, Surabaya 60115, IndonesiaBiomedical Engineering Study Program, Department of Physics, Faculty of Science and Technology, Airlangga University, Surabaya 60115, IndonesiaGraduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe 657-8501, JapanGraduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe 657-8501, JapanSubjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of impairment-level recognition in several studies. This study continues our previous work on the use of EMG to recognize finger movement impairment levels. In contrast to our previous work, this study provided a better and more reliable recognition result with improved experimental settings, such as an increased sampling frequency, EMG channels, and extensive patient data. This study employed two data processing mechanisms, inter-subject cross-validation (ISCV) and data-scaled inter-subject cross-validation (DS-ISCV), resulting in two evaluation methods. The machine learning algorithms employed in this study were SVM, random forest (RF), and multi-layer perceptron (MLP). MLP_ISCV achieved the highest average recall score of 0.73 across impairment levels in the spherical grasp task. Subsequently, the highest average recall score of 0.72 among non-majority classes was achieved by SVM_DS-ISCV in the mass extension task. The cross-validation result shows that the proposed method effectively handled the imbalanced dataset without being biased toward the majority class. The proposed method demonstrated the potential to assist doctors in clarifying the subjective assessment of finger movement impairment levels.https://www.mdpi.com/2076-3417/14/23/10830electromyographyfinger movementFugl-Meyer assessmentimbalance dataimpairment levelpost-stroke patients |
| spellingShingle | Adhe Rahmatullah Sugiharto Suwito P Ayumi Ohnishi Yudith Dian Prawitri Riries Rulaningtyas Tsutomu Terada Masahiko Tsukamoto Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG Applied Sciences electromyography finger movement Fugl-Meyer assessment imbalance data impairment level post-stroke patients |
| title | Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG |
| title_full | Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG |
| title_fullStr | Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG |
| title_full_unstemmed | Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG |
| title_short | Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG |
| title_sort | evaluation of finger movement impairment level recognition method based on fugl meyer assessment using surface emg |
| topic | electromyography finger movement Fugl-Meyer assessment imbalance data impairment level post-stroke patients |
| url | https://www.mdpi.com/2076-3417/14/23/10830 |
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