AutoMCA: A Robust Approach for Automatic Measurement of Cranial Angles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Head posture assessment commonly involves measuring cranial angles, with photogrammetry favored for its simplicity over CT scans or goniometers. However, most photo-based measurements remain manual, making them time-consuming and inefficient. Existing automatic measuring approaches often requires specific markers and clean backgrounds, limiting their usability. We present AutoMCA, a robust automatic measurement system for cranial angles using accessible markers and tolerating typical indoor backgrounds. AutoMCA integrates MediaPipe Pose, a machine-learning solution, for head–neck segmentation and applies color thresholding and morphological operations for marker detection. Validation tests demonstrated Pearson correlation coefficients above 0.98 compared to manual Kinovea measurements for both the craniovertebral angle (CVA) and cranial rotation angle (CRA), confirming high accuracy. Further validation on individuals with neck disorders showed similarly strong correlations, supporting clinical applicability. Speed comparison tests revealed that AutoMCA significantly reduces measurement time compared to traditional photogrammetry. Robustness tests confirmed reliable performance across varied backgrounds and marker types. In conclusion, AutoMCA measures head posture efficiency and lowers the requirements for instruments and space, making the assessment more versatile and applicable.
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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 | 0.001 | 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 it