Monitoring Individual Erector Spinae Fatigue Responses Using Electromyography and Near Infrared Spectroscopy
Bibliographic record
Abstract
This study examined the utility of electromyography and near-infrared spectroscopy (NIRS) in assessing m. erector spinae activity during the Biering-Sorensen Back Muscle Endurance (BSME) test. Six men and four women (27.0 +/- 7.1 years of age) performed the BSME test (time = 131.5 +/- 43.5 s). EMG was used to quantify neuromuscular activity of the right and left side at the L3 level, and root mean square was scaled for maximum value at the start of the exercise. NIRS was used to evaluate blood volume (BV) and oxygenation (OX) simultaneous with EMG bilaterally at the L3 level. There was a decrease to 49+/- 8% of initial median frequency (mean= 83 Hz) on both right and left sides when the exercise was 90% complete, and the slope of the median frequency/time relationship was significantly related to BSME time (r = 0.82). Group means for BV increased during back exercise while OX decreased and was significantly different between right and left sides of the lower back. However, large OX response differences among individuals and between right and left sides were noted. OX and median frequency were moderately related (r = 0.27-0.38). It appears that NIRS combined with EMG is a promising tool for assessing localized metabolic and neuromuscular activity during static contractions of the lower back.
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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 | 0.000 | 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".