Deep Learning-Based Standardized Evaluation and Human Pose Estimation: A Novel Approach to Motion Perception
Bibliographic record
Abstract
Motion perception, pivotal to myriad specialized tasks, necessitates the enhancement of proficiency through sustained repetition of perception-action cycles to meet standard benchmarks.Attaining advanced skill levels often demands additional practice.However, traditional pedagogical and evaluation systems predominantly hinge on the subjective experiences of instructors and evaluators.This dependence precipitates two principal challenges in the domain of motion perception-related professional work.Firstly, learners grapple with securing timely, adequate guidance during learning and practice, given the slow, trial-and-error nature of key point acquisition.Secondly, objective evaluation, fraught with instability, fails to consistently deliver accurate quantitative assessments, thereby adversely impacting the learning process.In response to these challenges, this study introduces a deep learning-based approach for standardized evaluation and human pose estimation.The methodology begins with the utilization of OpenPose for body joint detection.This is followed by a Deep Neural Network (DNN)-informed strategy for posture information extraction.Lastly, leveraging our team's extensive experience in dance instruction, a novel method for describing and discerning differences in dance movements is proposed.This approach enables a quantitative evaluation and provides intuitive feedback on the mechanics of dance movements, thereby enhancing the monitoring of participants' progress.Validated experimentally, the proposed methodology demonstrates precision in motion perception and quantitative evaluation.It not only offers practical guidance for enhancing the quality of dance instruction but also provides a valuable reference for other applications.
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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) | 0.001 | 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".