Version 1
: Received: 1 October 2024 / Approved: 2 October 2024 / Online: 2 October 2024 (10:48:35 CEST)
How to cite:
Suwito P, A. R. S.; Ohnishi, A.; Prawitri, Y. D.; Rulaningtyas, R.; Terada, T.; Tsukamoto, M. Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG. Preprints2024, 2024100115. https://doi.org/10.20944/preprints202410.0115.v1
Suwito P, A. R. S.; Ohnishi, A.; Prawitri, Y. D.; Rulaningtyas, R.; Terada, T.; Tsukamoto, M. Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG. Preprints 2024, 2024100115. https://doi.org/10.20944/preprints202410.0115.v1
Suwito P, A. R. S.; Ohnishi, A.; Prawitri, Y. D.; Rulaningtyas, R.; Terada, T.; Tsukamoto, M. Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG. Preprints2024, 2024100115. https://doi.org/10.20944/preprints202410.0115.v1
APA Style
Suwito P, A. R. S., Ohnishi, A., Prawitri, Y. D., Rulaningtyas, R., Terada, T., & Tsukamoto, M. (2024). Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG. Preprints. https://doi.org/10.20944/preprints202410.0115.v1
Chicago/Turabian Style
Suwito P, A. R. S., Tsutomu Terada and Masahiko Tsukamoto. 2024 "Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG" Preprints. https://doi.org/10.20944/preprints202410.0115.v1
Abstract
Subjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of recognition research in several studies. This paper continues our previous work on a recognition method of finger movement impairment levels using EMG. In contrast to our previous work, this paper provided a better recognition result with an improved experimental setting, such as higher sampling frequency and number of EMG channels. A large number of patients was recruited to provide a reliable recognition performance. This paper 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 employed machine-learning algorithms are SVM, random forest (RF), and multi-layer perceptron (MLP). The highest average recall score across impairment levels of 0.73 was achieved by MLP_ISCV in the spherical grasp task. Subsequently, the highest average recall score of non-majority classes of 0.72 was achieved by the SVM_DS-ISCV in the mass extension task. The cross-validation result showed 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 finger movement impairment level.
Public Health and Healthcare, Physical Therapy, Sports Therapy and Rehabilitation
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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