Non-invasive Techniques for Muscle Fatigue Monitoring: A Comprehensive Survey
Bibliographic record
Abstract
Muscle fatigue represents a complex physiological and psychological phenomenon that impairs physical performance and increases the risks of injury. It is important to continuously monitor fatigue levels for early detection and management of fatigue. The detection and classification of muscle fatigue also provide important information in human-computer interactions (HMI), sports injuries and performance, ergonomics, and prosthetic control. With this purpose in mind, this review first provides an overview of the mechanisms of muscle fatigue and its biomarkers and further enumerates various non-invasive techniques commonly used for muscle fatigue monitoring and detection in the literature, including electromyogram (EMG), which records the muscle electrical activity during muscle contractions, mechanomyogram (MMG), which records vibration signals of muscle fibers, near-infrared spectroscopy (NIRS), which measures the amount of oxygen in the muscle, ultrasound (US), which records signals of muscle deformation during muscle contractions. This review also introduces the principle and mechanism, parameters used for fatigue detection, application in fatigue detection, and advantages and disadvantages of each technology in detail. To conclude, the limitations/challenges that need to be addressed for future research in this area are presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".