Identification of the Human Postural Sway Response to Visual Inputs
Bibliographic record
Abstract
This study investigated how the human body responds to visual sensory information in upright standing. Our efforts were focused on using a linear system identification approach in which the input was the position profile of the visual perturbation through virtual reality, and the output was the body angle through measuring the hip displacement. The results unveiled that the underlying system acts as a band-pass filter over the frequency range of interest. The findings demonstrated that by increasing the amplitude of the perturbations, the gain between the body angle output and the visual input decreased, indicating a nonlinear behavior. Also, the continuously diminishing phase of the frequency response revealed the presence of a delay of 150 ms for visual contributions. Additionally, a new detrending method using the Fourier Transform was implemented to remove nonstationarities, achieving a more accurate model. Moreover, an initial investigation showed a significant distinction in responses to the presented visual input between the two groups of subjects.Clinical Relevance- The proposed research offers insights into complex underlying mechanisms of human postural control in the presence of visual perturbations. Consequently, this study has the potential to contribute to the rehabilitation process for patients with balance disorders due to visual sensory impairments by re-educating or emphasizing more on the visual sensory modality.
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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".