Combining transcutaneous spinal stimulation and functional electrical stimulation increases force generated by lower limbs: When more is more
Bibliographic record
Abstract
Background: Transcutaneous Spinal Stimulation (TSS) has been shown to promote activation of the lower limb and trunk muscles and is being actively explored for improving the motor outcomes of people with neurological conditions. However, individual responses to TSS vary, and often the muscle responses are insufficient to produce enough force for self-supported standing. Functional electrical stimulation (FES) can activate individual muscles and assist in closing this functional gap, but it introduces questions regarding timing between modalities. Methods: To assess the effects of TSS and FES on force generation, ten neurologically intact participants underwent (1) TSS only, (2) FES only, and (3) TSS + FES. TSS was delivered using four electrodes placed at T10-T11 through the L1-L2 intervertebral spaces simultaneously, while FES was delivered to the skin over the right knee extensors and plantarflexors. For all conditions, TSS and FES were delivered using three 0.5 ms biphasic square-wave pulses at 15 Hz. During the TSS + FES condition, timing between the two modalities was adjusted in increments of ¼ time between pulses (16.5 ms). Results: When TSS preceded FES, a larger force production was observed. We also determined several changes in muscle activation amplitude at different relative stimulus intervals, which help characterize our finding and indicate the facilitating and inhibitory effects of the modalities. Conclusions: Utilizing a delay ranging from 15 to 30 ms between stimuli resulted in higher mean force generation in both the knee and ankle joints, regardless of the selected FES location (Average; knee: 112.0%, ankle: 103.1%).
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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) | 0.000 | 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) | 0.003 | 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".