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Sensors
Volume 25
Issue 22
10.3390/s25226829
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Automated EMG-Based Classification of Upper ExtremityMotor Impairment Levels in Subacute Stroke

by
Alexey Anastasiev
1,
Hideki Kadone
2,*,
Aiki Marushima
3,
Hiroki Watanabe
3,
Alexander Zaboronok
3,
Shinya Watanabe
3,
Akira Matsumura
4,
Kenji Suzuki
5,
Yuji Matsumaru
3,
Hiroyuki Nishiyama
6 and
Eiichi Ishikawa
3
1
Department of Neurosurgery, University of Tsukuba Hospital, University of Tsukuba, 2-1-1 Amakubo, Tsukuba 305-8576, Ibaraki, Japan
2
Center for Cybernics Research (CCR), Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
3
Department of Neurosurgery, Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
4
Ichihara Hospital, 3681 Ozone, Tsukuba 300-3295, Ibaraki, Japan
5
Artificial Intelligence Laboratory, Center for Cybernics Research, Institute of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8573, Ibaraki, Japan
6
Center for Cyber Medicine Research, University of Tsukuba, 1-1-1 Amakubo, Tsukuba 305-8575, Ibaraki, Japan
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(22), 6829; https://doi.org/10.3390/s25226829 (registering DOI)
Submission received: 29 September 2025 / Revised: 4 November 2025 / Accepted: 5 November 2025 / Published: 7 November 2025
(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)
Versions Notes

Abstract

Rehabilitation of upper extremity (UE) impairments after stroke requires regular evaluation, with standard methods typically being time–consuming and relying heavily on manual assessment by therapists. In our study, we propose automating these assessments using electromyography (EMG) as a core indicator of muscle activity, correlating passive and active EMG signals with clinical motor impairment scores. UE motor function in 25 patients was evaluated using the Fugl–Meyer Assessment for UE (FMA–UE), the Modified Ashworth Scale (MAS), and the Brunnstrom Recovery Stages (BRS). EMG data were processed via feature extraction and linear discriminant analysis (LDA), with 10-fold cross–validation for binary classification based on clinical score thresholds. The LDA classifier accurately distinguished impairment categories, achieving area under the receiver operating characteristic curve (AUC–ROC) scores of 0.897 ± 0.272 for FMA–UE > 33, 0.981 ± 0.103 for FMA–UE > 44, 0.890 ± 0.262 for MAS > 0, 0.968 ± 0.130 for BRS > 3, and 0.987 ± 0.085 for BRS > 4. Notably, resting–state EMG alone yielded comparable classification performance. These findings demonstrate that EMG–driven assessments can reliably classify motor impairment levels, offering a pathway to objective clinical scoring that can streamline rehabilitation workflows, reduce therapists’ manual burden, and prioritize patient recovery over assessment procedures.
Keywords: stroke; upper extremity; hemiparesis; motor assessment; outcome assessment; automated assessment; machine learning; supervised learning; electromyography (EMG) stroke; upper extremity; hemiparesis; motor assessment; outcome assessment; automated assessment; machine learning; supervised learning; electromyography (EMG)

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MDPI and ACS Style

Anastasiev, A.; Kadone, H.; Marushima, A.; Watanabe, H.; Zaboronok, A.; Watanabe, S.; Matsumura, A.; Suzuki, K.; Matsumaru, Y.; Nishiyama, H.; et al. Automated EMG-Based Classification of Upper ExtremityMotor Impairment Levels in Subacute Stroke. Sensors 2025, 25, 6829. https://doi.org/10.3390/s25226829

AMA Style

Anastasiev A, Kadone H, Marushima A, Watanabe H, Zaboronok A, Watanabe S, Matsumura A, Suzuki K, Matsumaru Y, Nishiyama H, et al. Automated EMG-Based Classification of Upper ExtremityMotor Impairment Levels in Subacute Stroke. Sensors. 2025; 25(22):6829. https://doi.org/10.3390/s25226829

Chicago/Turabian Style

Anastasiev, Alexey, Hideki Kadone, Aiki Marushima, Hiroki Watanabe, Alexander Zaboronok, Shinya Watanabe, Akira Matsumura, Kenji Suzuki, Yuji Matsumaru, Hiroyuki Nishiyama, and et al. 2025. "Automated EMG-Based Classification of Upper ExtremityMotor Impairment Levels in Subacute Stroke" Sensors 25, no. 22: 6829. https://doi.org/10.3390/s25226829

APA Style

Anastasiev, A., Kadone, H., Marushima, A., Watanabe, H., Zaboronok, A., Watanabe, S., Matsumura, A., Suzuki, K., Matsumaru, Y., Nishiyama, H., & Ishikawa, E. (2025). Automated EMG-Based Classification of Upper ExtremityMotor Impairment Levels in Subacute Stroke. Sensors, 25(22), 6829. https://doi.org/10.3390/s25226829

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