Muscle fatigue during cross country sprint assessed by activation patterns and electromyographic signals time–frequency analysis
Bibliographic record
Abstract
The aims of this study were as follows: (i) analysis of activation patterns during the spurt of two heats of a cross country skiing sprint with the double poling technique and (ii) quantification of muscle fatigue of the investigated muscles. Eight elite skiers were tested and surface electromyographic signals (EMG) were recorded from seven muscles of the upper and lower limbs. For each subject and each muscle, the activation intervals were calculated for relying on a double-threshold statistical detector and the average rectified value was calculated on each activation interval. The detected activations were processed by a time-frequency algorithm in order to assess the progression of muscle fatigue. The EMG activation patterns and EMG amplitude highlighted no significant difference between the two spurts, despite a generally lower speed in the second spurt. The frequency analysis showed that upper body muscles are the first to be affected by fatigue and that clear signs of muscle fatigue appear right from the first spurt of the sprint simulation (i.e., biceps and triceps brachii) with a decrease in the instantaneous mean frequency. Biceps brachii activations and fatigue demonstrated the involvement of this muscle in propulsion.
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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".