Pose-based Hand Movement Tracking for Monitoring Psoriatic Arthritis Progression
Bibliographic record
Abstract
Psoriatic arthritis (PsA) is a chronic inflammatory disease characterized by joint pain, stiffness, and reduced mobility, significantly impacting patients' quality of life.Accurate and continuous monitoring of hand mobility is crucial for assessing disease progression and therapy effectiveness.Traditional assessment methods often rely on subjective patient reports or sporadic clinical evaluations, leading to potential gaps in data and delayed therapeutic adjustments.This paper introduces a pose-based method for early detection and monitoring of psoriatic arthritis by analyzing hand closure movement videos recorded on smartphones.The proposed framework utilizes the Google MediaPipe Hand model to extract 3D hand joint coordinates from video sequences, which are then used to compute closing and stretching scores of the hand.These scores are derived using distance-based and angle-based metrics to quantify finger mobility, with a dedicated quality control mechanism ensuring that only videos meeting specific orientation and frame criteria are analyzed.Datasets comprising psoriasis patients and healthy individuals reveal that while the closing score offers robust and normalized measurements independent of anatomical variability, the stretching score requires lateral-view recordings for improved sensitivity.The results underscore the potential of this non-invasive, real-time tool to aid in early clinical intervention and long-term disease management.Future work will focus on integrating lateral-view analysis and joint thickness measurements for enhanced diagnostic accuracy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".