AI-driven personalized training model for dance movement and health management research
Bibliographic record
Abstract
In this paper, a personalized scheme recommendation method for dance movements based on ontological similarity is proposed.An ontology model of trainers is established, and in order to explore the interactions between trainers' attribute features and their influence on core parameters, SWRL rules are established using Jena inference engine for the inference of core parameters of training programs.The similarity degree is calculated according to the different types of user variables respectively, and the artificial neural network model is used to determine the degree of similarity between different trainers, in order to complete the recommendation of personalized training programs for dance movements.And then the requirements of the system are summarized to achieve the framework construction of the personalized dance movement training program recommendation system to achieve the health management in the training process.The recommendation effects presented by the similarity calculation method of this paper have reached the design goal of this paper, and the personalized recommendation system of this paper has also significantly improved the physical fitness level and the performance effect of dance skills of the experimental group of dance trainees, and the success rate of the kicking back leg movement has reached 91.67%.However, the system's function of improving health knowledge and health awareness needs to be further upgraded.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".