Alternate foot placement strategies: Training effects on the avoidance of multiple planar obstacles
Bibliographic record
Abstract
In a cluttered environment an undesirable foothold may have various seemingly equal alternant options for avoidance, however it is not chance that determines the method we employ (Patla et al., 1999). The objective of this study was to determine individuals' foot placement selections when avoiding two consecutive ground-level obstacles. Three groups of 12 participants were collected; non-athletes (those with no previous training and/or sport participation), field athletes, and dancers. All participants (female, age 21.4 ± 1.96) walked along a 13m by 6m travel path toward a goal. On 50% of the trials, participants had to avoid stepping on two consecutive planar obstacles (20cm wide by 60cm long) that appeared mid-way along the path (via a ceiling mounted projector) located where participants would normally step. Three possible conditions were randomly presented: 1) obstacles appeared during steady state locomotion (~4 steps from start); 2) obstacles appeared when participants were 2 steps away; and 3) no obstacles presented (straight walk through/wash out trials). Most avoidance involved cross-over stepping (i.e. medial-medial step combination (MM) or one of two possible steering strategies; medial-lateral (ML) or lateral-medial (LM) combination). Delaying the appearance of the obstacles resulted in a greater occurrence of MM behaviour across all groups. When considering the variability of the strategy used across condition and between the groups, the non-athletes and dancers appear to reduce their variability in stepping strategy from steady state to the N-2 condition, where the field athletes became more variable. It would appear that when reducing the time given to implement an avoidance that the natural tendency is to produce a MM behaviour. Training may influence this behaviour, such that avoidance variability in sport may be viewed as a strength but a detriment during dance.
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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) | 0.002 | 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".