Survey on enhancing athletic training with activity recognition and deep learning
Bibliographic record
Abstract
The proposed methodology focuses on the advanced analysis and enhancement of athletic techniques, particularly targeting the precision, angle, and positioning of movements such as shooting. By integrating technologies like Human Action Recognition (HAR), Artificial Intelligence (AI), and Deep Learning, the system analyzes player data from images or videos, identifying errors and offering insights for improvement. It suggests optimal shooting angles, positions for maximum scoring, and corrective measures to refine technique, thus aiding coaches in player assessment, strategy formulation, and tactical decision-making. The methodology employs OpenCV and Machine Learning algorithms for accurate performance analysis, while Deep Learning models, such as Artificial Neural Networks (ANN) and You Only Look Once (YOLOv8), optimize feature extraction and analysis. YOLOv8, an advanced computer vision framework, ensures precise detection of key attributes. These combined technologies enable the identification of performance flaws and guide athletes toward achieving their goals. The solution is developed using Python, OpenCV, HAR, and YOLOv8, with IDEs like VSCode and Jupyter Notebook facilitating its implementation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".