Interleaved neuromuscular electrical stimulation reduces muscle fatigue
Bibliographic record
Abstract
INTRODUCTION: Neuromuscular electrical stimulation (NMES) can be delivered over a muscle belly (mNMES) or nerve trunk (nNMES). Both methods generate contractions that fatigue rapidly due, in part, to non-physiologically high motor unit (MU) discharge frequencies. In this study we introduce interleaved NMES (iNMES), whereby stimulus pulses are alternated between mNMES and nNMES. iNMES was developed to recruit different MU populations with every other stimulus pulse, with a goal of reducing discharge frequencies and muscle fatigue. METHODS: Torque and electromyography were recorded during fatigue protocols (12 min, 240 contractions) delivered using mNMES, nNMES, and iNMES. RESULTS: Torque declined significantly 3 min into iNMES and 1 min into both mNMES and nNMES. Torque decreased by 39% during iNMES and by 67% and 58% during mNMES and nNMES, respectively. CONCLUSIONS: iNMES resulted in less muscle fatigue than mNMES and nNMES. Delivering NMES in ways that reduce MU discharge frequencies holds promise for reducing muscle fatigue during NMES-based rehabilitation. Muscle Nerve, 2016 Muscle Nerve 55: 179-189, 2017.
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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) | 0.002 | 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".