Spectral analysis of balance control and postural adaptation with and without the addition of different degrees of cognitive loading in healthy children and young adults
Bibliographic record
Abstract
The purpose of this study was to examine the effect of different degrees of cognitive loading on balance control and postural adaptations in ten children (8-10 years of age) and ten university aged adults. A 2 (children versus adults) x 2 (quiet standing versus lean task) x 3 (cognitive task 1 versus cognitive task 2 versus no cognitive task) mixed factorial design with repeated measures on the last two factors was used. An AMTI force platform recorded the Center of Pressure (COP) movement. Traditional measures included area of sway (Ao), path length (L), anterior-posterior(AP) and lateral sway (Lt). Fourier analysis was used to compute frequency and power distributions of postural sway. The balance tasks included quiet standing assessing balance control, and a leaning task used for the assessment of postural adaptations. The leaning task required participants to lean as far as possible forward and backward, from the vertical, while maintaining balance. Object identification and numeric classification task were used to manipulate the degree of cognitive loading. A total of 18 trials were completed by each participant with each trial lasting 20 seconds. The results will be discussed in the context of current theories of motor control as applied to attention and control of posture.
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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 | 0.002 |
| 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) | 0.001 | 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".