An Exploratory Approach to Manipulating Dynamic Stability: Investigating the Role of Visual Control during a Precision Foot Placement Task
Bibliographic record
Abstract
Bacgkground: The visual system acts as a feedforward control mechanism during human locomotion. Visual information contributes coordination of the head-arm-trunk (HAT) segment and modulating foot placement. The purpose of this study was to examine the effects of a complex navigational stone-stepping task on HAT segment control and how the visual system guides locomotion during a complex foot placement task. Methods: Nine university-aged females (Mean age: 22.5 years old +/-1.75) participated in this study. Participants were outfitted with four rigid bodies and two IRED markers in order to measure kinematic data, as well as an ASL H7-HS High Speed Head Mounted Optics eye tracking unit to assess gaze behaviour. Participants performed 40 trials across four conditions (e.g. predetermined and self-selected pathways; starting with either the left or the right foot), on a 7.2mx1.2m raised-target platform. Measurements were compared across conditions (e.g. constrained versus unconstrained), time points (e.g. first, middle, and last trial performed of each condition), and segment (one versus two). Results: Findings revealed that there was a significant difference between conditions such that: 1) the constrained vertical pupil RMS velocity was higher than the unconstrained (F(3,24)=4.71; p= .04; d=.46); 2) the unconstrained horizontal pupil RMS velocity was higher than the unconstrained (F(3,24)=4.40; p= .03; d=.36); 3) the constrained average walking speed was greater than the unconstrained (F(3,24)=23.27; p=0.04; d=.30); 4) the constrained trunk pitch was greater than the unconstrained (F(3,21)=4.84; p=0.01; d=.45); and 5) the unconstrained dynamic stability margin minimum was greater than the constrained (F(3,21)=4.89; p= .01; d=.41). Conclusions: During constrained trials, there was evidence to suggest that trunk control had a greater regulation than during unconstrained trials. However, during unconstrained trials, individuals were able to choose footholds based on their current state of stability. Thus, conditional demands influenced gaze behaviour, separating eye movements into vertical or horizontal components. Meeting abstract presented at VSS 2016
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".