AI-Enabled High-Level Layer for Posture Recognition Using The Azure Kinect in Unity3D
Bibliographic record
Abstract
Posture recognition is one of the challenging tasks in computer vision. It lays on top of the pose estimation of the different body joints, and can be used in many applications. In the medical field, it can serve to assist patients in rehabilitation. In games it can be an elegant form of computer human interaction. Different Artificial Intelligence techniques were used over the years to precisely output the joint positions of the body from a single or stream of images. One of the great solutions that tacked well the pose estimation challenge is the Kinect camera, however further process is required to create and detect body postures. This article presents a customizable high-level layer that allows its users to easily create and manage body postures in unity3d projects allowing them to focus more on the other aspects of their project. The layer offers two detection methods, both scored more than 95% accuracy in each of the tested postures.
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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 | 0.000 | 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 | 0.001 |
| 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".