Determining perception thresholds of young adults to small continuous moving platform perturbations
Bibliographic record
Abstract
ABSTRACT Detecting external disturbances is vital for maintaining balance, as corrective actions are initiated to prevent falls. Quantifying people’s ability to perceive such disturbances improves our understanding of how balance is maintained. This study aims to: 1) quantify healthy young adults’ ability to perceive external perturbations while balancing on a stabilometer, and 2) understand the relationship between balance performance and perturbation magnitude relative to participants’ perception threshold. Participants (n=22; 20–35 years) completed a multiple staircase protocol. While standing on a stabilometer mounted on a moving platform, they attempted to keep it horizontal during 10-second trials with small continuous perturbations. After each trial, participants were asked whether they perceived the platform movement. Perturbation magnitudes were adjusted for the next trial based on their response. This process continued for each staircase until the termination criteria were met, at which point participants’ individual perception threshold was determined. Participants then performed ten 40-second trials on the stabilometer, two trials in each condition: without perturbation, perturbation at the 100%, 80%, and 50% of the individual’s perception threshold, and the pilot study’s minimum threshold. Balance performance was defined as time-in-balance ratio and RMS deviation angle from horizontal. Perception thresholds varied significantly between participants individuals, with an RMS acceleration ranging from 2.67 and 12.80 cm/s 2 . The results showed that perturbation magnitude has a significant correlation with variability in deviation angle (R=0.24, p=0.0038). The results suggest that some participants can perceive very small perturbations during a challenging balance task. Subthreshold perturbations, although very small, can influence balance performance. NEW & NOTEWORTHY To our knowledge, this is the first study to quantitatively measure the conscious perception threshold of platform perturbations while doing a balancing task. We found that young healthy adults with little similar prior experience in tasks close to stabilameter balancing task can detect small platform perturbations while doing such challenging task. The results also showed that platform perturbations below conscious threshold can influence the balance performance on the stabilometer.
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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 | 0.003 |
| 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) | 0.003 | 0.001 |
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".