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Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation
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Dynamic Muscle State-Driven Framework for Intelligent Grading of Chronic Trapezius Fatigue.

Yujia Chen, Ling He, Guanci Yang

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |April 3, 2026
    PubMed
    Summary

    Prolonged sedentary work causes chronic shoulder muscle fatigue. This study introduces a novel grading framework using muscle tone and stiffness to quantify fatigue severity, aiding early intervention.

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    Myo-mechanical Analysis of Isolated Skeletal Muscle
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    Area of Science:

    • Biomedical Engineering
    • Musculoskeletal Biomechanics
    • Computational Physiology

    Background:

    • Prolonged sedentary work frequently leads to chronic shoulder muscle fatigue and persistent pain.
    • Objective quantification of fatigue is crucial for effective intervention, but current methods lack standardized multidimensional assessment.
    • The trapezius muscle, a shoulder girdle stabilizer, is a common pain site, necessitating focused research.

    Purpose of the Study:

    • To propose a novel muscle mechanical state-based grading framework for chronic trapezius muscle fatigue.
    • To objectively quantify cumulative fatigue severity using dual biomarkers: muscle tone and stiffness.
    • To develop intelligent assessment models for detecting and grading chronic muscle fatigue.

    Main Methods:

    • A two-phase experimental paradigm to analyze temporal evolution and correlation of muscle tone and stiffness.
    • Development of a multi-level grading system based on dynamic regulatory capacity reflected in muscle mechanical data.
    • Implementation of three dual-branch graph-structured models, culminating in a HG-GCN model with cross-attention and gated fusion.

    Main Results:

    • The proposed framework effectively characterized muscle rigidity using muscle tone and stiffness as dual biomarkers.
    • The HG-GCN model demonstrated high classification accuracies: 88.67% for middle trapezius and 89.17% for lower trapezius.
    • The intelligent assessment models successfully detected and quantified chronic fatigue in the trapezius muscle.

    Conclusions:

    • The novel muscle mechanical state-based grading framework provides a standardized, multidimensional tool for assessing chronic muscle fatigue.
    • The developed HG-GCN model shows significant potential for accurate and objective quantification of trapezius muscle fatigue.
    • This research facilitates early detection and intervention for chronic fatigue associated with sedentary work, potentially reducing persistent pain.