Reducing Muscle Fatigue Due to Functional Electrical Stimulation Using Random Modulation of Stimulation Parameters
Bibliographic record
Abstract
A major limitation of many functional electrical stimulation (FES) applications is that muscles tend to fatigue very rapidly. It was hypothesized that FES-induced muscle fatigue could be reduced by randomly modulating the pulse frequency, amplitude, and pulse width in a range of +/-15%. Seven subjects with spinal-cord injuries participated in this study. FES was applied to quadriceps and tibialis anterior muscles using surface electrodes. Isometric force was measured, and the time for the force to drop by 3 dB (fatigue time) was compared between trials. Four different modes of FES were applied in random order: constant stimulation, randomized frequency, randomized amplitude, and randomized pulse width. There was no significant difference between the fatigue-time measurements for the four modes of stimulation (P=0.329). Therefore, random modulation appeared to have no effect. Based on an observed correlation between maximum force measurements and trial order, we concluded that having 10-min rest periods between trials was insufficient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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".