Improved Yoga Pose Detection Using MediaPipe and MoveNet in a Deep Learning Model
Bibliographic record
Abstract
The escalating global embrace of yoga as a holistic approach to well-being has accentuated the demand for refined and efficient techniques in yoga posture recognition. Traditional manual methods, although valuable, have exhibited protracted timelines and vulnerability to inaccuracies. In response, we introduce an innovative solution that harnesses the capabilities of deep learning (DL) models, elevating both the precision and accuracy of posture detection. Our approach predominantly leverages the Thunder variant of the MoveNet model, renowned for its exceptional proficiency in distinguishing an array of yoga postures. This model is seamlessly amalgamated with the MediaPipe technique, facilitating adept keypoint identification and skeletonization. In our proposed framework, input images undergo initial preprocessing, followed by skeletonization achieved through keypoint extraction. This pivotal process enables the encapsulation of distinctive points intrinsic to individual yoga poses. Central to our methodology is the incorporation of the large and diverse yoga (LDY) dataset, which encompasses five distinct yoga pose categories: Downdog, Goddess, Plank, Tree, and Warrior. A thorough evaluation demonstrates our approach's outstanding accuracy of 99.50% when deployed on the LDY dataset. As maintaining precise posture is pivotal in averting immediate discomfort and mitigating long-term health complexities, the implications of this advancement are profound. It charts a course toward more meticulous and accessible mechanisms for detecting yoga poses, thus profoundly influencing the physical and mental well-being of practitioners.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.002 |
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".