Development of an Automated Sports Teaching Assistance Tool Based on Image Recognition
Bibliographic record
Abstract
In the context of deep integration between nationwide fitness initiatives and education informatization, traditional sports teaching faces challenges such as low efficiency in manual instruction and imprecise movement assessment.The advancement of image recognition technology provides a solid foundation for the intelligent transformation of sports education.However, existing studies still face bottlenecks in terms of motion recognition accuracy and the practical utility of teaching assistance tools.Traditional machine learning approaches rely heavily on handcrafted features, making it difficult to capture the spatiotemporal complexity of sports movements.While deep learning models have shown promise, they often overlook higher-order correlations among human body joints and the temporal dependencies of action sequences, resulting in suboptimal performance in recognizing dynamic and interactive movements.Moreover, current tools generally lack intuitive and effective modules for visualizing movement information.To address these issues, this study focuses on the development of an automated sports teaching assistance tool based on image recognition.The main contributions include: (1) proposing an enhanced hypergraph convolutional network that models higher-order joint correlations and incorporates temporal feature learning to improve the recognition accuracy of complex sports movements; and (2) designing a multidimensional motion information visualization scheme, enabling dynamic motion trajectory display and key joint deviation analysis to provide intuitive feedback for both teaching and learning.The research outcomes are expected to break through the spatial and temporal limitations of traditional instruction and establish a precise, personalized support system for sports education, offering both theoretical and technical support for its digital transformation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".