Low-intensity functional electrical stimulation can increase multidirectional trunk stiffness in able-bodied individuals during sitting
Bibliographic record
Abstract
The inability to voluntarily control the trunk musculature is a major problem following spinal cord injury as it can compromise functional independence and produce unwanted secondary complications. Recent developments suggest that neuroprostheses utilizing functional electrical stimulation (FES) may be able to facilitate or restore trunk control during sitting, standing, and other tasks involving postural control. In spite of these efforts, no study to date has used low-intensity FES to increase multidirectional trunk stiffness and damping in an attempt to bolster stability while minimizing muscle fatigue. Therefore, we set out to investigate how multidirectional trunk stiffness changes in response to low-intensity FES of a few selected trunk muscles. Fifteen healthy participants sitting naturally were randomly perturbed in eight horizontal directions. Trunk stiffness and damping during natural and FES-supported sitting conditions were quantified using force and trunk kinematics in combination with two models of a mass-spring-damper system. Our results indicate that low-intensity FES can increase trunk stiffness in healthy individuals, and this specifically for directions associated with the stimulated muscles. In contrast, trunk damping was not found to be altered during FES. The presented results suggest that low-intensity FES is a simple and effective method for increasing trunk stiffness on demand.
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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) | 0.001 | 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".