temporal quantitative analysis of visuomotor behavior during different twisting somersaults in elite and sub-elite trampolinists
Bibliographic record
Abstract
To perform twisting somersaults and land safely, athletes need advanced spatial orientation skills in which vision might play a key role.Elite gymnasts translate more accurately the visual information into an appropriate kinematic response, thereby increasing their performance.Due to this link between vision and performance, it appears of interest to better understand the visuomotor strategies used by athletes during acrobatics as it could help coaches better guide their athletes through visuomotor skill development.The present study sought to identify the differences in gaze behavior between elite and sub-elite trampolinists during the execution of four acrobatics of increasing difficulty.Seventeen trampolinists (8 elites, 9 sub-elites) were equipped with 17 inertial measurement units and a wearable eye-tracker.Firstly, six typical metrics extracted from their body and gaze kinematics were analyzed.A mixed analysis of variance (ANOVA) was performed with the Expertise as inter-subject and the Acrobatics as intra-subject factors.Only one significant difference was observed in the Expertise factor: elite athletes fixated their gaze more often than sub-elite athletes (p=0.033),although the fixation durations were not different between the two groups.Secondly, to complement the understanding of trampolinists' visual strategies, more advanced eyetracking metrics were analyzed: the dwell time on areas of interest, the scan path on the trampoline bed, the temporal evolution of the gaze orientation endpoint (SPGO), and the time spent executing specific neck and eye strategies.Large inter-and intra-individual visuomotor variabilities were observed in the SPGO, which suggests that an ideal visual strategy to perform acrobatics does not exist.Notably in this study, while analyzing the combined eyes and neck movements, it was possible to confirm the use of spotting at the beginning and end of the acrobatics and to reveal a unique sport-specific visual strategy that we termed self-motion detection, which consists in not moving the eyes during fast head rotations.Self-motion detection was mainly used during the twisting phase of the acrobatics.This study proposes a thorough exploration of trampolinists' gaze behavior in highly ecological settings and contributes to enhancing the understanding of visuomotor strategies adopted during the execution of twisting somersaults.
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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 | 0.000 |
| 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 | 0.000 |
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".