Nonlinear analysis of the effects of vision and postural threat on upright stance
Bibliographic record
Abstract
BACKGROUND: Humans maintain upright stance to interact with their surroundings. Linear analysis of these processes fails to address the nonstationary behavior of the human body, whereas dynamical nonlinear approaches consider the underlying dynamics of postural sway. Here, behavior during upright stance was examined using nonlinear methods to provide additional insights into the effects of postural threat (height above ground) and vision on postural control. RESEARCH QUESTION: Can nonlinear methods provide more comprehensive identification and analysis of height- and vision-related changes in quiet standing? METHODS: This study is a secondary analysis of a previously collected and published dataset. Twenty young healthy adults performed a 60 s quiet standing balance task under all combinations of: (1) two height conditions: standing at ground level (LOW) and standing 3.2 m above ground level (HIGH); and (2) two visual conditions: eyes open (EO) and eyes closed (EC). Recurrence quantification analyses (RQA) were performed on centre of pressure (COP) and centre of mass (COM) data to calculate Recurrence Rate (%REC), Determinism (%DET), Entropy (ENT), and Average Line Length (LINE). RESULTS: For COP, %DET, ENT, and LINE showed significant effects of vision and height, with the values of each measure being lowest in the HIGH-EC condition. For COM, %DET, ENT, and LINE showed significant effects for vision, with the values of each being lower in the EC compared to EO condition; additionally, %DET was significantly smaller in the HIGH condition compared to LOW. SIGNIFICANCE: RQA identified differences in sway dynamics across both vision and height conditions that linear methods failed to detect. They represented greater randomness and adaptability in response to increased fear (HIGH) or decreased sensory information (EC). The novel effects observed with these RQA variables suggest nonlinear analyses provide a more robust tool for identifying the effects of postural threat and vision on upright stance.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".