Analysis of pressure distribution at the body–seat interface in able-bodied and paraplegic subjects using a deformable active contour algorithm
Bibliographic record
Abstract
In this paper, a semi-automatic method for segmenting pressure distribution image-based data at the body-seat interface is presented. The purpose of this work was to estimate the surface and the load supported by the ischial tuberosity (IT) region. The proposed method involves three steps: (1) detecting the IT region using a pressure-distribution image gradient; (2) estimating the contour of the IT region by an iterative active contour algorithm and finally (3) estimating the percentage of the surface and the weight-bearing of the IT region in a group of able-bodied (AB) and spinal-cord injury (SCI) subjects. It was found in this study that the weight bearing on the IT for the spinal-cord injured group is distributed on half the surface in comparison with the AB group or the powered wheelchair users groups. The findings of this study provide insights concerning pressure distribution in sitting for the paraplegic and able-bodied.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".