Method to Reduce Muscle Fatigue During Transcutaneous Neuromuscular Electrical Stimulation in Major Knee and Ankle Muscle Groups
Bibliographic record
Abstract
BACKGROUND: A critical limitation with transcutaneous neuromuscular electrical stimulation as a rehabilitative approach is the rapid onset of muscle fatigue during repeated contractions. We have developed a method called spatially distributed sequential stimulation (SDSS) to reduce muscle fatigue by distributing the center of electrical field over a wide area within a single stimulation site, using an array of surface electrodes. OBJECTIVE: To extend the previous findings and to prove feasibility of the method by exploring the fatigue-reducing ability of SDSS for lower limb muscle groups in the able-bodied population, as well as in individuals with spinal cord injury (SCI). METHODS: SDSS was delivered through 4 active electrodes applied to the knee extensors and flexors, plantarflexors, and dorsiflexors, sending a stimulation pulse to each electrode one after another with 90° phase shift between successive electrodes. Isometric ankle torque was measured during fatiguing stimulations using SDSS and conventional single active electrode stimulation lasting 2 minutes. RESULTS: We demonstrated greater fatigue-reducing ability of SDSS compared with the conventional protocol, as revealed by larger values of fatigue index and/or torque peak mean in all muscles except knee flexors of able-bodied individuals, and in all muscles tested in individuals with SCI. CONCLUSIONS: Our study has revealed improvements in fatigue tolerance during transcutaneous neuromuscular electrical stimulation using SDSS, a stimulation strategy that alternates activation of subcompartments of muscles. The SDSS protocol can provide greater stimulation times with less decrement in mechanical output compared with the conventional protocol.
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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 | 0.001 |
| 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".