Arousal pattern analysis of an Olympic champion in ski jumping
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mental strength is essential to success in many sports disciplines, especially in professional ski jumping. While physiological signals can reveal information on the mental state, their measurement and analysis for elite ski jumping athletes during competition has not been realised. For the first time in professional ski jumping, we investigated heart rate (HR), its temporal pattern, and corresponding body motion in relation to arousal of the Olympic ski jumping gold medallist Simon Ammann during actual competitions, including his Vancouver 2010 Winter Olympics victory. Using a miniature, on-body ECG monitor with integrated acceleration sensor, we collected a dataset of 99 hours length, including 37 hill jumps. Arousal was assessed from HR data conditioned on body position and acceleration data. The HR and its pattern were analysed during competition days, actual jump situations (training, qualification, and competition) and pre-performance routines. HR was related to the competitiveness of the jump situation, even when physical sports performance remained unchanged. Arousal during jumping and pre-performance routines showed highly reproducible HR patterns. The HR pattern, as assessed by dynamic time warping, deviated during the final Olympic jump, at which time the athlete reported difficulties in regulating arousal in his trained manner. Our approach can be used to collect, analyse, and visualise data to assess an athlete's levels and patterns of arousal during typical competitive situations. We believe that data collected in field-based studies with on-body sensing technology could assist in the design of arousal assessment tools and help facilitate top performance levels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it