A Real-Time System for Athlete Pose Analysis and Feedback Based on Machine Learning
Bibliographic record
Abstract
Accurate and real-time analysis of athlete posture is an important topic in sports-related image processing.In practical training environments, pose analysis systems are expected to operate under strict real-time constraints while remaining robust to occlusion, motion blur, and scene variation.However, existing approaches face several limitations.0054hreedimensional pose estimation often depends on large amounts of annotated data, which are expensive and difficult to obtain.Lightweight models designed for real-time inference tend to sacrifice spatiotemporal feature representation, leading to reduced accuracy.In addition, current feedback mechanisms are usually loosely connected to the underlying pose features and therefore provide limited diagnostic value.To address these issues, a real-time pose analysis and feedback framework based on self-supervised spatiotemporal optimization is presented.The system adopts a three-stage architecture consisting of a lightweight image feature extraction and two-dimensional keypoint detection module, a dual-path spatiotemporal feature refinement module, and a sequence-based feedback generation module.The refinement stage combines adaptive graph convolution for skeletal topology modeling with a lightweight spatiotemporal Transformer for learning temporal image features.Temporal coherence across video frames is exploited to construct self-supervised constraints for three-dimensional pose learning without manual annotations.Pose sequences are further matched with standard motion templates using dynamic time warping, and the resulting deviations are translated into structured feedback.The proposed framework reduces the dependence on annotated data, maintains real-time performance on edge devices, and provides interpretable feedback linked directly to pose deviations.Experimental results demonstrate that the system achieves a balanced trade-off between efficiency, accuracy, and practical usability in real training scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".