YoPose: Yoga Posture Recognition Using Deep Pose Estimation
Bibliographic record
Abstract
Originated in India, yoga is considered a spiritual practice as it brings flexibility, balance, and harmony to both physical and mental health. It becomes the art of healthy living. A variety of positions (also known as asanas) are exercised. Each of them is designed to provide a particular benefit to the body. In contrast, any incorrect action during a yoga session can be harmful to muscles and ligaments. As people are more comfortable with the home workout, the need for an instructor to assess the accuracy of a movement or posture is turned into a need for an auto-guiding framework. Human pose estimation is an important field of research in computer vision. It serves several applications, extending from health monitoring to public safety. As it tackles multiple challenges related to the human posture, it can be used to identify yoga asanas. In this study, we develop a deep-learning self-instruction yoga classifier named YoPose. Based on the pose information, the proposed framework helps individuals to improve their yoga postures by providing personalized feedback. A public dataset including six asanas is used to train and evaluate the model. The introduction of transfer learning and a data augmentation scheme helped to achieve promising results with more than a 98% accuracy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".