In Situ Motion Capture of Speed Skating: Escaping the Treadmill
Bibliographic record
Abstract
The advent of the Kinect depth imager has opened the door to motion capture applications that would have been much more costly with previous technologies. In part, the Kinect achieves this by focusing on a very specific application domain, thus narrowing the requirement for the motion capture system. Specifically, Kinect motion capture works best within a small physical space while the camera is stationary. We seek to extend Kinect motion capture for use in athletic training - speed skating in particular - by placing the Kinect on a mobile, robotic platform to capture motion in situ. Athletes move over large distances, so the mobile platform addresses the limited viewing area of the Kinect. As the platform moves, we must also account for the now dynamic background against which the athlete performs. The result is a novel, visually-guided robotic platform that follows athletes, allowing us to capture motion and images that would not be possible with a treadmill. We describe the system in detail and give examples of the system capturing the motion of a speed skater at typical training speeds.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".