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Robust fatigue markers obtained from muscle synergy analysis
Chen Zhang1, Zi-Jian Zhou2, Lu-Yi Wang1
1Research Center of Exercise Capacity Assessment and Promotion, School of Sports Science and Physical Education, Northeast Normal University, Changchun, Jilin Province, China.
Experimental Brain Research
|August 13, 2024
Summary
Nonnegative matrix factorization identified muscle synergy biomarkers for fatigue. These indices, including activation phase difference and coactivation area, reflect central nervous system and muscle activity changes post-exertion.
Area of Science:
- Biomechanics
- Motor Control
- Exercise Physiology
Background:
- Muscle fatigue significantly impacts motor control and performance.
- Analyzing muscle synergies offers insights into neuromuscular adaptations during exertion.
- Current methods for fatigue biomarker detection require refinement.
Purpose of the Study:
- To apply nonnegative matrix factorization (NNMF) for muscle synergy analysis.
- To identify novel biomarkers of muscle fatigue from synergy structures.
- To investigate the relationship between these biomarkers and fatigue intensity.
Main Methods:
- 11 participants performed leg press exercise to induce fatigue.
- Collected surface electromyography (sEMG), ECG, Borg scale, and motion data.
- Derived and validated three indices: activation phase difference, coactivation area, and coactivation time.
Main Results:
- Median frequency (MDF) declined in primary muscles, indicating fatigue.
- Significant differences in activation phase difference, coactivation area, and time were observed post-fatigue.
- Activation phase difference and coactivation area correlated significantly with fatigue intensity.
Conclusions:
- NNMF-derived muscle synergy indices serve as effective biomarkers for muscle fatigue.
- These biomarkers reflect concurrent changes in central nervous system and muscle activity.
- Coactivation area demonstrated effectiveness in single-leg landing validation.
