Validity of a Computer Postural Analysis to Estimate 3-Dimensional Rotations and Translations of the Head From Three 2-Dimensional Digital Images
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to describe and evaluate the validity/accuracy of the computerized system PosturePrint for measuring head posture. METHODS: Computer analysis was compared with 125 measured positions of a mannequin head in 5 degrees of freedom. For each mannequin position, 3 digital photographs were obtained (left lateral, anteroposterior, and right lateral) and were processed through the PosturePrint computer system. For the head analysis, a headgear with 3 reflective markers was placed on a subject; and there were additional click-on markers at the ear tragus, upper lip, acromioclavicular joints, and episternal notch. Head postures were calculated as lateral translation (T(x)), lateral flexion (R(z)), axial rotation (R(y)), flexion-extension (R(x)), and anterior-posterior translation (T(z)). For an error analysis, PosturePrint algorithm calculations were compared with the true mannequin head positions. Furthermore, average head posture was determined in student volunteers (n = 40). RESULTS: Mean computational errors were R(x) = 1.3 degrees (SD 0.6 degrees) and T(z) = 1.1 mm (SD 0.5 mm) for sagittal displacements and R(y) = 1.1 degrees (SD 0.7 degrees), R(z) = 0.6 degrees (SD 0.4 degrees), and T(x) = 1.1 mm (SD 0.5 mm) for frontal view displacements. For the normal group, mean head displacements were 1.1 degrees or less for all rotations and 1 mm or less for lateral translations (T(x)); and forward head posture (T(z)) averaged 3 cm. CONCLUSION: From the mannequin positions, small mean errors indicate that the PosturePrint system is accurate. In the future, statistical research determining the correlation between head displacements, neck pain, function, and health status should be performed.
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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".