Contribution of visual, vestibular, and somatosensory systems to gait termination strategy
Bibliographic record
Abstract
During human locomotion, availability of the online information about the environment and the body limbs (relative to each other and to the environment) is an important issue for the central nervous system (CNS) to modulate the motion strategies to control and maintain the postural balance. There are three main feedback systems, which update the CNS about the environmental and the body position relative to the surrounding space during performing any task, such as Gait Termination (GT). Those controlling systems are: visual, vestibular, and somatosensory systems (Dietz, 1992, Patla, 2003). Visual system contributes in feedforward control as an anticipatory strategy for dynamic stability for navigation in the environment (Patla, 2003). In reactive control, however, the information from the visual system is not fast enough to recover the body equilibrium from an unexpected perturbation such as slip (Patla, 2003). The vestibular system, detect the perturbation by head acceleration and activate extensor synergies to support the body. Moreover, the somatosensory information from the foot mechanoreceptors and load sensitive afferents in the joints provide feedback from the perturbation and send signal to initiate a polysynaptic response (Patla 2003; Oates et al., 2005). The focus of this study was to have a better understanding about the role of each of the three sensory systems in postural control during GT strategy. METHODS
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".