Design and testing of an instrumentation system to reduce stimulus pulse amplitude requirements during FES
Bibliographic record
Abstract
Functional Electrical Stimulation (FES) has been used for decades to restore muscle function following neural trauma. Another promising use has been to maintain or increase muscle strength following injury. Unfortunately in the latter case there is considerable stimulation pain for the sensorially intact subject during effective levels of stimulation using surface electrodes. Recent research [1][2] has suggested using a constant long (up to 10 ms) low amplitude or ramped conditioning pulse just prior to the high amplitude stimulus pulse (100 - 200 microsec) to reduce the excitably of sensory nerve fibers. However, commercial muscle stimulators cannot be easily modified to provide such complex pulse patterns and flexible pulse train control. We have designed and implemented a novel, very flexible LabVIEW based monophasic constant current muscle stimulator that provides pulse trains with long duration prepulses and high voltage stimulus pulses with selectable shapes, amplitudes, durations and frequencies. As well, the stimulator system includes an isolated EMG amplifier to record the evoked M-waves, which are used to estimate the fraction of muscle motor units being stimulated. We are presently testing this system, by stimulating the median nerve of human subjects and measuring the evoked M-waves for a range of stimulus pulse amplitudes, preceded by either ramped or rectangular low amplitude prepulses. The results indicate that, rather than inhibiting the activation of motor axons, as has been suggested [4], the prepulses at very low amplitudes excite these axons to subthreshold levels. The amplitudes of the stimulus pulses can then be significantly reduced while still achieving high levels of muscle activation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".