Improving Accuracy and Runtime of Skeletal Tracking of Lower Limbs for Athletic Jump Mechanics Assessment
Bibliographic record
Abstract
Previous studies have shown that athletic jump mechanics assessments are valuable tools for identifying indicators of an individual's anterior cruciate ligament injury risk. These assessments, such as the drop jump test, often relied on camera systems or sensors that are not always accessible nor practical for screening individuals in a sports setting. As human pose estimation deep learning models improve, we envision transitioning biometrical assessments to mobile devices. As such, here we have addressed two of the most preclusive hindrances of the current state-of-the-art models: accuracy of the lower limb joint prediction and the slow run-time of in-the-wild inference. We tackle the issue of accuracy by adding a post-processing step that is compatible with all inference methods that outputs 3D key points. Additionally, to overcome the lengthy inference rate, we propose a depth estimation method that runs in real-time and can function with any 2D human pose estimation model that outputs COCO key points. Our solution, paired with a state-of-the-art model for 3D human pose estimation, significantly increased lower-limb positional accuracy. Furthermore, when paired with our real-time joint depth estimation algorithm, it is a plausible solution for developing the first mobile device prototype for athlete jump mechanics assessments.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".