Reducing muscle fatigue during transcutaneous neuromuscular electrical stimulation by spatially and sequentially distributing electrical stimulation sources
Bibliographic record
Abstract
PURPOSE: A critical limitation with transcutaneous neuromuscular electrical stimulation is the rapid onset of muscle fatigue. We have previously demonstrated that spatially distributed sequential stimulation (SDSS) shows a drastically greater fatigue-reducing ability compared to a single active electrode stimulation (SES). The purposes of this study were to investigate (1) the fatigue-reducing ability of SDSS in more detail focusing on the muscle contractile properties and (2) the mechanism of this effect using array-arranged electromyogram (EMG). METHODS: SDSS was delivered through four active electrodes applied to the plantarflexors, sending a stimulation pulse to each electrode one after another with 90° phase shift between successive electrodes. In the first experiment, the amount of exerted ankle torque and the muscle contractile properties were investigated during a 3 min fatiguing stimulation. In the second experiment, muscle twitch potentials with SDSS and SES stimulation electrode setups were compared using the array-arranged EMG. RESULTS: The results demonstrated negligible torque decay during SDSS in contrast to considerable torque decay during SES. Moreover, small changes in the muscle contractile properties during the fatiguing stimulation using SDSS were observed, while slowing of muscle contraction and relaxation was observed during SES. Further, the amplitude of the M-waves at each muscle portion was dependent on the location of the stimulation electrodes during SDSS. CONCLUSION: We conclude that SDSS is more effective in reducing muscle fatigue compared to SES, and the reason is that different sets of muscle fibers are activated alternatively by different electrodes.
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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 | 0.000 |
| 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) | 0.001 | 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".