Enhancing Digital Video Analysis of Bar Kinematics in Weightlifting: A Case Study
Bibliographic record
Abstract
Weightlifting technique can be objectively assessed from two-dimensional video recordings. Despite its importance, participants' bar trajectories in research involving the snatch or clean exercises are often not reported, potentially due to the time required to digitize video. The purpose of this investigation was to evaluate the use of a light-emitting diode (LED)-based marker, digital video, and open-source software to automatically track the bar end during weightlifting exercises. A former national-level weightlifter was recorded with a digital video camera performing the snatch, clean and jerk, and squat exercises. An LED-based marker was placed on the right end of the bar. This marker was automatically tracked using 2 open-source software programs to obtain vertical and horizontal position coordinates. The LED-based marker was successfully auto-tracked for all videos over a variety of camera settings. Furthermore, the vertical and horizontal bar displacements and vertical bar velocity were consistent between the 2 software programs. This study demonstrates that an LED-based marker can be automatically tracked using open-source software. This combination of an LED-based marker, consumer camera, and open-source software is an accessible, low-cost method to objectively evaluate weightlifting technique.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".