Synergistic effects of transcutaneous spinal stimulation and neuromuscular electrical stimulation on lower limb force production: Time to deliver
Bibliographic record
Abstract
BACKGROUND: Transcutaneous spinal stimulation (TSS) and neuromuscular electrical stimulation (NMES) can facilitate self-assisted standing in individuals with paralysis. However, individual variability in responses to each modality may limit their effectiveness in generating the necessary leg extension force for full body weight standing. To address this challenge, we proposed combining TSS and NMES to enhance leg extensor muscle activation, with optimizing timing adjustment to maximize the interaction between the two modalities. METHODS: To assess the effects of TSS and NMES on knee extension and plantarflexion force, ten neurologically intact participants underwent three conditions: (1) TSS control, (2) NMES control, and (3) TSS + NMES. TSS was delivered between the T10 and L2 vertebrae, while NMES was delivered to the skin over the right knee extensors and plantarflexors. TSS and NMES were administered using a 15 Hz train of three 0.5 ms biphasic pulses. During the TSS + NMES condition, the timing between modalities was adjusted in increments of ¼ the interval within a 15 Hz frequency, i.e., 66, 49.5, 33, 16.5, and 1 ms. RESULTS: NMES combined with TSS, produced synergistic effects even on non-targeted muscle groups, thereby promoting leg extension across multiple joints in the kinematic chain. The sequence of NMES or TSS trains relative to each other did not significantly impact motor output. Notably, a delay of 16.5 to 49.5 ms between interleaved TSS and NMES pulses, each delivered at 15 Hz, results in more robust and synergistic responses in knee extensors and plantarflexors. CONCLUSIONS: By adjusting the timing between TSS and NMES, we can optimize the combined use of these modalities for functional restoration. Our findings highlight the potential of integrated TSS and NMES protocols to enhance motor function, suggesting promising avenues for therapeutic applications, particularly in the rehabilitation of individuals with SCI.
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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) | 0.002 | 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".