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Related Experiment Video

Updated: Jan 9, 2026

Muscle Function Obtained with Motion Mode Ultrasound and Surface Electromyography during Core Endurance Exercise
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Correlation Analysis Based on Muscle sEMG Time-frequency Characteristics and Ultrasound-measured Muscle Mass

Zijing You, Guilan Chen, Haiqiong Luo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    Surface electromyography (sEMG) shows moderate to strong correlations with muscle quality indicators. This technology holds promise for wearable, low-cost muscle health monitoring and disease diagnosis.

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    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
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    Area of Science:

    • Biomedical Engineering
    • Kinesiology
    • Sports Science

    Background:

    • Skeletal muscle quality assessment is crucial for evaluating health and function.
    • Simple, portable tools for muscle quality monitoring are needed in clinical settings.
    • Surface electromyography (sEMG) is used for muscle activation and function, but its link to muscle quality indicators needs further exploration.

    Purpose of the Study:

    • To investigate the relationship between various sEMG features and muscle quality indicators.
    • To assess the potential of sEMG for predicting muscle thickness (MT) and cross-sectional area (CSA).

    Main Methods:

    • Collected sEMG data during maximum voluntary contraction (MVC) from six leg muscles in 16 subjects.
    • Extracted 14 sEMG features, including root mean square (RMS) and probability density function (PDF).
    • Utilized ultrasound (US) to measure MT and CSA, and employed Pearson correlation and LASSO regression for analysis.

    Main Results:

    • Most sEMG features exhibited moderate to strong correlations (0.3 ≤ |r| ≤ 0.7) with muscle parameters (MVC, MT, CSA).
    • The LASSO model achieved promising prediction accuracy for quadriceps femoris (QF) and hamstring (HS) CSA (R²adj = 0.75, 0.87).
    • Good regression results were observed for HS MT (R²adj = 0.61).

    Conclusions:

    • sEMG technology demonstrates significant potential for non-invasive muscle health monitoring and functional assessment.
    • The findings suggest sEMG can serve as a valuable tool for wearable, low-cost muscle health monitoring and disease diagnosis.