A Dual-Fatigue Speed Guidance Model for Isokinetic Training: Cognitive and sEMG-Based Physiological Fatigue Approach

Isokinetic training has been proven to be an effective method in rehabilitative therapy. However, the quantitative relationship between training speed and the biophysical condition of the human body is not well established. To address this, a biceps isokinetic training robot implemented the dual-fatigue speed guidance model that integrated cognitive fatigue perception and physiological fatigue index based on sEMG was proposed. The sEMG signals were transformed from time series to a Markov state transition field (MTF) expressed as a state transition network diagram, from which modularity (MD) was extracted as a physiological fatigue index. MD combined with cognitive perceptions by the Borg RPE scale, formed a dual-fatigue speed guidance model for isokinetic training. After one month of training with nine male participants, the proposed model demonstrated improvements in peak torque, muscle dimensions, and cognitive fatigue compared to constant speed training, with a significant absolute peak torque increase (P = 0.0427). The proposed method enables personalized configuration of sports rehabilitation and enhancement, and it can be expected to apply in clinical precision therapy with more participants.

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Publication Details

Journal
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tnsre.2025.3578304
Citations
1
Primary Topic
Occupational Health and Safety Research
Type
article
Field-Weighted Citation Impact
1.54

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A Dual-Fatigue Speed Guidance Model for Isokinetic Training: Cognitive and sEMG-Based Physiological Fatigue Approach

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A Dual-Fatigue Speed Guidance Model for Isokinetic Training: Cognitive and sEMG-Based Physiological Fatigue Approach

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article en
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Abstract

Isokinetic training has been proven to be an effective method in rehabilitative therapy. However, the quantitative relationship between training speed and the biophysical condition of the human body is not well established. To address this, a biceps isokinetic training robot implemented the dual-fatigue speed guidance model that integrated cognitive fatigue perception and physiological fatigue index based on sEMG was proposed. The sEMG signals were transformed from time series to a Markov state transition field (MTF) expressed as a state transition network diagram, from which modularity (MD) was extracted as a physiological fatigue index. MD combined with cognitive perceptions by the Borg RPE scale, formed a dual-fatigue speed guidance model for isokinetic training. After one month of training with nine male participants, the proposed model demonstrated improvements in peak torque, muscle dimensions, and cognitive fatigue compared to constant speed training, with a significant absolute peak torque increase (P = 0.0427). The proposed method enables personalized configuration of sports rehabilitation and enhancement, and it can be expected to apply in clinical precision therapy with more participants.

IEEE Transactions on Neural Systems and Rehabilitation EngineeringVol. 33
Shenzhen Institute of Information Technology (CN), Shanghai Jiao Tong University (CN), Harbin Institute of Technology (CN), Peng Cheng Laboratory (CN), Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 18%
Occupational Health and Safety Research
1.54
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