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Article 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.
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| http://dx.doi.org/10.1109/TNSRE.2025.3578304 | DOI Listing |
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