Accuracy of the Automated Range of Motion Observer and Reporter Software for Fully Automated Joint Measurement From Patient Videos
Bibliographic record
Abstract
INTRODUCTION: Accurate assessment of joint range of motion (ROM) is essential for diagnosing and managing upper extremity injuries. Universal goniometers are the most used tools for measuring ROM, but they require skilled observers and are limited by interobserver variability. An automated system for measuring joint range of motion from patient videos could facilitate standardized reporting of outcomes after reconstructive surgery. METHODS: An Automated Range of Motion Observer and Reporter (ARMOR) software was developed as an autonomous, video-based ROM measurement tool leveraging OpenCV pose estimation. ARMOR was used to assess upper extremity range of motion and was validated against photography-based (photogoniometry) and manual goniometry in a cohort of brachial plexus surgery patients. RESULTS: The correlation coefficients comparing ARMOR to manual goniometry were above 0.90 for all motion tasks, except for elbow flexion. For shoulder flexion, the mean difference between ARMOR and manual goniometry was more than 14° smaller than the difference for photogoniometry. Mean differences for shoulder abduction were similar between ARMOR and photogoniometry. CONCLUSION: ARMOR's accuracy in assessing shoulder ROM, independence from human observer bias, and telemedicine compatibility makes it a promising solution for consistent and accessible ROM assessment. The autonomous nature of the software enhances the data collection workflow for clinical researchers while eliminating interrater variability.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".