Muscle fatigue detection using near-infrared spectroscopy and electromyography
Bibliographic record
Abstract
Abstract Introduction: Muscle fatigue is often experienced by athletes and in work settings. Excessive fatigue can lead to injury and musculoskeletal disorders. Surface electromyography (EMG) is typically used to detect and ultimately prevent fatigue during isometric movement. The application of EMG to fatigue detection in dynamic movement requires, however, a secondary confirmation of fatigue based on physiological measures. Our objective was to determine if muscle oxygenation derived via near-infrared spectroscopy (NIRS) was correlated with relevant EMG indicators of neuromuscular fatigue and whether observed correlations were related to the fatigue process. Methods: Bilateral electromyograms from three upper leg muscles and the tissue oxygenation index (TOI) of the vastus lateralis muscle were recorded in sixteen non-disabled individuals during cycle ergometry to volitional exhaustion. Six EMG activity features were extracted and the Pearson correlation coefficient between each feature and TOI was determined. Results: The EMG root mean square, spectral standard deviation, second spectral moment, and zero-crossing rate (ZC) were strongly correlated with TOI. The time course of ZC and the correlation of this feature with TOI suggest that there could be a relation between muscle oxygenation and fatigue. Conclusion: Future work should use the knowledge gained in this study to investigate whether NIRS can be used to verify the onset of fatigue as detected by EMG.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".