Athlete movement recognition based on deep learning: Exploration combined with biodynamics
Bibliographic record
Abstract
Sports movement recognition is vital for performance assessment, training optimization, and injury prevention, but manual observation is slow and inconsistent. We propose a compact framework that fuses deep learning with biodynamic analysis: convolutional neural networks (CNNs) extract spatial cues from video, a biodynamic encoder derives joint angles, torques, velocities, and forces, and temporal convolutional networks (TCNs) capture sequential dependencies. Using a simulated multimodal dataset of athletic activities, our method outperforms baseline CNN and LSTM models, achieving higher precision (91.5), recall (93.2), and accuracy (92.7). Gains are largest for complex biomechanics (e.g., throwing, kicking), with up to a 10% accuracy increase from biodynamic integration. These results highlight the value of multimodal fusion and provide a scalable path toward real-time, AI-driven sports performance monitoring, with potential extensions to niche sports (fencing, gymnastics, pole vaulting, javelin).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".