Enhancing Winter Sport Activities: Improving the Visual Perception and Spatial Awareness of Downhill Winter Athletes with Augmented Reality Headset Displays
Bibliographic record
Abstract
Strong spatial-awareness and visual perception skills can improve an athlete's performance, enhance training routines and reduce the potential for injury due to physical error.This research study looks to investigate the barriers and design requirements for developing an augmented reality headset display for downhill winter athletes, which may improve visual perception, spatial-awareness and reduce injury.This research used a variety of human-centred-design methods to collect the participant data, including surveys, experience-simulation-testing, userresponse-analysis, and statistical analysis.During the study, 34 participants with previous experience skiing, or snowboarding at different skill-levels, wore an augmented reality headset and evaluated the visual perception of an icon, while watching a simulation video and standing on a slope changing platform to simulate a downhill skiing experience.The study revealed that various levels of downhill winter athletes may benefit differently from access to athletic data during a physical activity, and indicated that some expert level athletes can train to strengthen their spatial-awareness abilities.The results generated visual design recommendations, including icon colours, locations within the field-of-view, and alert methods which could be utilized to optimize the usability of a headset display.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".