Evaluating the Intensity of Muscle Contraction by Near‐Infrared Spectroscopy, a Potential Application for Scaling Muscle Spasm
Bibliographic record
Abstract
Muscle spasticity, common in conditions such as cerebral palsy, spinal cord injury, and multiple sclerosis, is traditionally assessed using the Modified Ashworth Scale, which lacks consistency. This study evaluates near-infrared spectroscopy (NIRS) as a non-invasive tool for measuring muscle contraction intensity. Thirty-seven healthy adults performed isometric contractions at varying intensities (15%, 30%, 45%, and 60% of maximal voluntary contraction), with NIRS sensors monitoring changes in the Tissue Oxygenation Index (TOI) and electromyography (EMG) measuring muscle activity. Results demonstrated a significant negative correlation between contraction intensity and ΔTOI, indicating that higher contraction levels resulted in greater reductions in muscle oxygenation. Additionally, a multinomial logistic regression model confirmed that TOI could reliably predict contraction intensity (p < 0.001). This technique could provide real-time, objective data for spasticity assessment, potentially improving treatment plans.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".