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Record W4405336679 · doi:10.1016/j.jhsa.2024.10.006

Augmented Reality-Based Finger Joint Range of Motion Measurement: Assessment of Reliability and Concurrent Validity

2024· article· en· W4405336679 on OpenAlexafffund
Sasha Létourneau, Helen Jingshu Jin, Ruby Grewal, Douglas C. Ross, Caitlin Symonette

Bibliographic record

VenueThe Journal Of Hand Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsWestern University
FundersWestern University
KeywordsConcurrent validityMedicineRange of motionReliability (semiconductor)Metacarpophalangeal jointPhysical medicine and rehabilitationFinger jointJoint (building)ValidityAugmented realityOrthodonticsThumbPhysical therapyArtificial intelligenceSurgeryPsychometricsComputer scienceClinical psychologyStructural engineeringEngineeringPatient satisfaction

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.115
GPT teacher head0.336
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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