Predicting muscle anaerobic threshold by using a wearable optical sensor during exercise
Bibliographic record
Abstract
The anaerobic threshold (AT) is a point during intense exercise that can be used to predict muscular fatigue. Determining the AT non-invasively helps to adjust exercise intensity and prevent overuse injuries. Near-infrared spectroscopy (NIRS) is an optical technology that can provide real-time information about muscle oxidative metabolism. The objective of this pilot study was to investigate the relationship between NIRS parameters of muscle oxygenation and traditional measures of exercise monitoring, such as heart rate and relative body oxygen consumption (VO2). Healthy adults with moderate to high fitness levels participated in an incremental exercise protocol on a stationary bicycle. NIRS parameters were compared to ventilatory VO2 using a metabolic cart. Respiratory Exchange Ratio (RER) < 1.0 was used as a proxy for determining the AT. NIRS data were collected from the primary locomotor muscle (vastus lateralis - VL) and a control muscle (deltoid) using two wearable NIRS sensors. Heart rate data were collected by a wearable ECG sensor. The NIRS data showed a significant decline in VL muscle oxygenated hemoglobin (O2Hb) concentration (p<0.05) at one exercise stage after the AT was identified. Muscle O2Hb did not show a significant decrease in the deltoid at the AT. Furthermore, there were no noticeable changes in heart rate at the AT. Our results indicate that a wearable NIRS sensor can predict the AT in exercising muscles and may provide a localized measure of muscular fatigue during exercise.
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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 | 0.000 |
| 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".