Toward study of features associated with natural sleep posture using a depth sensor
Bibliographic record
Abstract
Estimating the sleeping posture of a person under natural resting environment is a very complex problem. In this study, a new non-invasive framework is proposed for exploring features defining sleep postures of a person. Three dimensional depth scans as well as the cross-sectional scans of the static sleeping body are developed using the measured depth data. These features can then be used in order to estimate the changes in patterns associated with the head, limbs and torso position in depth images. Scans of the sleeping postures are taken at various cross sectional directions. 1D and 2D Fast Fourier Transformation are explored in order to provide complementary feature of the scans. We also generate grids on the horizontal sleeping plane. In the depth image, each grid has a quadrangle configuration which can further be divided into two triangular meshes. We calculated a surface normal vector to each of the mesh. Given a 2D scan strip perpendicular for the sleeping plane, variations of the normal along such scan strips can be used as other local features. These selected features can be further integrated as a part of natural sleep posture estimation methods.
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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 | 0.000 |
| 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".