TY - JOUR
T1 - Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG
AU - P, Adhe Rahmatullah Sugiharto Suwito
AU - Ohnishi, Ayumi
AU - Prawitri, Yudith Dian
AU - Rulaningtyas, Riries
AU - Terada, Tsutomu
AU - Tsukamoto, Masahiko
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - electromyography
KW - finger movement
KW - Fugl-Meyer assessment
KW - imbalance data
KW - impairment level
KW - post-stroke patients
KW - recognition
UR - http://www.scopus.com/inward/record.url?scp=85211949630&partnerID=8YFLogxK
U2 - 10.3390/app142310830
DO - 10.3390/app142310830
M3 - Article
AN - SCOPUS:85211949630
SN - 2076-3417
VL - 14
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 23
M1 - 10830
ER -