High‐accuracy photogrammetric technique for human spine measurement
Bibliographic record
Abstract
Abstract Close range photogrammetry has been recognised as an essential tool for the capture of high‐accuracy spatial data for medical applications, in particular work involving dynamic human body parts such as limbs. Offline and online photogrammetric systems are readily available for a number of common applications. However, off‐the‐shelf systems are not always appropriate because of project site conditions. To achieve high measurement accuracy in a field environment, a modified field camera calibration technique was introduced. The technique is particularly important where each camera is limited to one captured image during calibration, as the camera and the calibration testfield are in fixed positions. In this paper a custom‐built imaging system designed for the study of the human spine in an outdoor environment is introduced. The discussion addresses: (1) imaging system design; (2) modified field calibration techniques; and (3) a case study on human spines. Two field camera calibration techniques were evaluated, both of which improved the accuracy of the prototype system, the use of a detachable target board offering the best results. This modified camera calibration procedure has improved the 3D measurement accuracy from 1·25 ± 0·3 mm to 0·43 ± 0·1 mm. The improvement is at a level achievable in the laboratory. The technique is considered to provide accurate and reliable anthropometric landmark measurement at low cost. This was evaluated in a clinical setting where diurnal changes in spine length and contour were measured in a cohort of 30 university students. The capability of the technique to measure sagittal and frontal angular changes provides a novel way of examining changes in spine shape.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".