The effect of random modulation of functional electrical stimulation parameters on muscle fatigue
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Bibliographic record
Abstract
Muscle contractions induced by functional electrical stimulation (FES) tend to result in rapid muscle fatigue, which greatly limits activities such as FES-assisted standing and walking. It was hypothesized that muscle fatigue caused by FES could be reduced by randomly modulating parameters of the electrical stimulus. Seven paraplegic subjects participated in this study. While subjects were seated, FES was applied to quadriceps and tibialis anterior muscles bilaterally using surface electrodes. The isometric force was measured, and the time for the force to drop by 3 dB (fatigue time) and the normalized force-time integral (FTI) were determined. Four different modes of FES were applied in random order: constant stimulation, randomized frequency (mean 40 Hz), randomized current amplitude, and randomized pulsewidth (mean 250 micros). In randomized trials, stimulation parameters were stochastically modulated every 100 ms in a range of +/-15% using a uniform probability distribution. There was no significant difference between the fatigue time measurements for the four modes of stimulation. There was also no significant difference in the FTI measurements. Therefore, our particular method of stochastic modulation of the stimulation parameters, which involved moderate (15%) variations updated every 100 ms and centered around 40 Hz, appeared to have no effect on muscle fatigue. There was a strong correlation between maximum force measurements and stimulation order, which was not apparent in the fatigue time or FTI measurements. It was concluded that a 10-min rest period between stimulation trials was insufficient to allow full recovery of muscle strength.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it