Minimizing muscle fatigue through optimization of electrical stimulation parameters
Bibliographic record
Abstract
Rapid onset of muscle fatigue in response to electrical stimulation is a major challenge when designing a neuroprosthesis. This study aimed to introduce a decision-making algorithm to optimize pulse amplitude and pulse duration, for current regulated electrical stimulators, to attain a target joint torque level while minimizing muscle fatigue. We measured ankle torque produced by different pairs of pulse amplitude and pulse duration applied to the plantar-flexors. In each session, we measured the maximum generated torque and calculated muscle fatigue (fatigue time and torque-time integral). Then, we determined the pulse amplitude and pulse duration pair that generated a target level of torque while minimizing muscle fatigue. High bilateral symmetry and day-to-day repeatability was observed for the torque time-series between the left and right plantar-flexors of each participant (median correlation coefficient = 0.95). Compared to the average fatigue obtained by various pulse amplitude and pulse duration pairs for a given level of torque, delivering pulses with the optimal pair reduced fatigue on average by 22.5% according to fatigue time and 6.6% according to torque-time integral. We created an empirical model describing how pulse amplitude and pulse duration can be modulated to generate specific levels of plantar-flexion torque with minimum muscle fatigue.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".