Data driven analysis of biomechanical factors associated with improved cross-country skiing performance
Bibliographic record
Abstract
The objective of this work was to implement a data-driven biomechanical approach that can assess the biomechanical determinants of cross-country skiing performance. To achieve this, full-body kinematic data were obtained and analyzed during over-ground cross-country skiing trials of varied efforts to quantify propulsion strategies, spatiotemporal coordination, drag, and joint power outputs. Eight athletes of varied skill levels were analyzed, encompassing a total of 5,568 movement cycles (i.e., propulsion strategies). To assess the many interacting modes of variation potentially associated with the skilled performance in cross-country skiing two complementary analyses were implemented. First, an automated objective classifier was trained on a subset of data to detect varied propulsion strategies associated with different athlete skill levels. Second, a principal component analysis was utilized to provide animated reconstructions of representative movement styles and relevant indicators of variance related to skill level. Results suggest that several factors were associated with skill-level including: (1) dominant propulsion strategy, (2) smaller frontal area, (3) reduced ski external rotation, (4) increased upper and lower body joint power. The data driven approaches implemented here can identify key features associated with cross-country skiing performance and have the capacity to be used in a sport-field setting to communicate efficient strategies to athletes.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".