Spatially Distributed Sequential Stimulation Reduces Fatigue in Paralyzed Triceps Surae Muscles: A Case Study
Bibliographic record
Abstract
Functional electrical stimulation (FES) is limited by the rapid onset of muscle fatigue caused by localized nerve excitation repeatedly activating only a subset of motor units. The purpose of this study was to investigate reducing fatigue by sequentially changing, pulse by pulse, the area of stimulation using multiple surface electrodes that cover the same area as one electrode during conventional stimulation. Paralyzed triceps surae muscles of an individual with complete spinal cord injury were stimulated, via the tibial nerve, through four active electrodes using spatially distributed sequential stimulation (SDSS) that was delivered by sending a stimulation pulse to each electrode one after another with 90° phase shift between successive electrodes. For comparison, single electrode stimulation was delivered through one active electrode. For both modes of stimulation, the resultant frequency to the muscle as a whole was 40 Hz. Isometric ankle torque was measured during fatiguing stimulations lasting 2 min. Each mode of stimulation was delivered a total of six times over 12 separate days. Three fatigue measures were used for comparison: fatigue index (final torque normalized to maximum torque), fatigue time (time for torque to drop by 3 dB), and torque-time integral (over the entire trial). The measures were all higher during SDSS (P < 0.001), by 234, 280, and 171%, respectively. The results are an encouraging first step toward addressing muscle fatigue, which is one of the greatest problems for FES.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".