Vestibular and Proprioceptive Contributions to Human Balance Corrections
Bibliographic record
Abstract
Movement strategies controlling quiet stance and rapid balance corrections may have common characteristics. We investigated this assumption for lower leg proprioceptive loss (PL), peripheral vestibular loss (VL), and healthy controls. Our underlying hypothesis was that changes in movement-strategy modulation following sensory loss would improve with prosthetic biofeedback. Quiet stance was measured under different sensory conditions and compared to corrections induced by multidirection support-surface tilts. Response synergies were assessed using electromyography recordings from several muscles. Biofeedback of trunk sway during gait and stance tasks used lower trunk rotations to drive head-band-mounted vibro-tactile and auditory actuators. Strategies of quiet stance were different for roll and pitch, depending on sensory conditions. Simultaneously acting strategies were observed for low- and high-frequency sway. PL induced strategies different from those of VL and controls. VL strategies were identical to those of controls but with greater amplitudes. Tilt perturbation movement strategies were similar to high-frequency strategies of quiet stance--multisegmental. VL induced increased trunk pitch and roll responses with hypermetric trunk muscle responses and hypometric knee responses but unchanged synergies. Increasing PL up the legs caused changed synergies. Biofeedback reduced stance body sway in VL and elderly subjects. In conclusion, several movement strategies underlie quiet stance with high-frequency strategies being common to those of perturbed stance. PL changes both movement strategies and synergies, whereas VL only causes pathological changes to the modulation depth. Thus, VL is more easily rectified using trunk sway positional biofeedback.
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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 | 0.001 | 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".