Poster Session I, July 14th 2010 — Abstracts Design of an ergometer to train and evaluate elite crosscountry skiiers
Bibliographic record
Abstract
Sport ergometers offer a reasonable alternative for semi-specific training conditions as it provides a sheltered environment to practice. Their additional values from in situ performances are mainly due to real time feedback of mechanical variables as the external power generated by athlete at one (or more) contact with the ergometer (e.g. handle power while rowing an ergometer). These variables are mainly recorded using force and displacement sensors. As a result, in many sport (e;g. rowing, cycling, running), these machine are also used for performance assessment and both physiological and biomedical research program. However, the design of a specific ergometer has to reproduce the dynamics of the in situ movement for an accurate mechanical analysis. A first step in such a way is to analyse the three-dimensional kinematics in order that the ergometer design simulate accurately the kinematic performed in situ. In cross-country skiing, the kinematics observed while skiing the actually available ergometers is far from the one performed during in situ conditions. Thus, the mechanical parameters measured while skiing these ergometers are not pertinent to analyze and discriminate the performance produce by elite athletes. This work presents an approach based on a 3D kinematics analysis to design an innovative ergometer fully instrumented to acutely train and evaluate elite cross-country skiers. 3D kinematics analysis of in situ skating, performed using three video cameras showed characteristic 3D trajectories of the stick during the contact period with the snow. The ergometer was design to reproduce this specific kinematics (two specific phases) by adding one dof in translation of the contact point between the rope with the ergometer. This rope connects skier’s hand to an airbraked flywheel to reproduced the resistance. A selfrecoiling system allows to perform the following skating cycle. An instrumentation coupled with a specific interface allows real time feedback of the power generated by skier at each hand. During the last two years, this ergometer was skiing by the french national teams to prepare Vancouver 2010. Further investigations must be undertaken to support the accuracy of this ergometer with in situ conditions and to still improve his design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.021 |
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".