3-D Kinematics Using Moving Cameras. Part 1: Development and Validation of the Mobile Data Acquisition System
Bibliographic record
Abstract
Many human activities, particularly sporting skills, occur over large distances. But technical limitations have forced biomechanists to conduct studies only on portions of such skills. In this paper we present the design and validation of a mobile data collection system composed of a camera cart that allows the tracking of athletes along a larger portion of their movements. A key feature of this system is that it requires only a small field of view to record and analyze joint motions. The validation of this method was conducted with three approaches. For all approaches, intermarker distances obtained from real measures were compared to those obtained from digitized video data. In all three experiments, the results proved to be within the accepted error range of 5%. The net differences between measured values and digitized values ranged from 0.8 to 3 mm, while the relative errors ranged from 0.2 to 6%. This first experimentation using a mobile camera array to collect and reconstruct biomechanical data has proven to be valid and worth pursuing for recording and analyzing ice hockey skating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".