Augmented Reality-Based Finger Joint Range of Motion Measurement: Assessment of Reliability and Concurrent Validity
Bibliographic record
Abstract
PURPOSE: This study aimed to determine the reliability and concurrent validity of finger joint range of motion (ROM) measurement using augmented reality (AR)-based hand tracking in a sample of healthy hands. Additionally, the study aimed to determine which camera view of the hand provided ROM measurements with the highest concurrent validity at each joint. METHODS: A web application developed for smart devices using Google's MediaPipe Hands framework converted AR-generated hand landmark coordinates from camera feed into ROM angle measurements in real time for all joints. From each of five camera views, we recorded five sets of AR-based flexion and extension measurements at the metacarpophalangeal (MCP), proximal interphalangeal (PIP) and distal interphalangeal (DIP) joints of normal index to small fingers. Test-retest reliability of the five AR-based measurements in each view was evaluated as was concurrent validity of AR-based measurements relative to manual goniometry, considered the reference standard. Given accepted inter-rater reliability of manual goniometry is 10°, we considered AR-based measurements within 10° of goniometry measurements to have "acceptable" concurrent validity. RESULTS: Forty-eight healthy hands (median age 31, 50% left, varying ethnicities) were measured. All joints demonstrated excellent test-retest reliability (intraclass correlation coefficient >0.75) in all views in flexion and ≥2 views in extension. AR-based flexion measurements were within 10˚ of goniometry in ulnar views of the index MCP, PIP, and DIP; the long MCP and PIP; and the ring PIP and DIP. In extension, multiple views at each joint consistently yielded AR-based measurements within 10° of goniometry. CONCLUSIONS: AR-based measurement has high concurrent validity and reliability; however, optimal camera views vary joint to joint. Validation in pathologic hands is required. CLINICAL RELEVANCE: Given its excellent reliability, AR-based measurement has potential for use in monitoring changes in finger ROM after intervention, either by clinicians in-person or by patients performing remote measurements independently.
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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 | 0.007 | 0.001 |
| 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".