Profiling finger-hand function of rheumatoid arthritis patients using a telerehabilitation gaming system
Bibliographic record
Abstract
The problem considered in this thesis is developing a set of digital features relevant in describing finger-hand function of early-onset rheumatoid arthritis (RA) patients. The premise is based on a novel telerehabilitation gaming system that operates on a store-and-forward design. The solution to this problem was to develop a full-scale gaming platform to examine client movement performance for precision aiming tasks based on a set of digital features. To complement the movement performance, still imagery in three unique poses are captured during a session to detect visual symptoms during disease activity and early warning signs of deformities that can arise from joint damage. Resulting data is gathered in a clinic or housed in a content management system where features are extracted and analyzed, providing reports/queries for care providers and allowing remote monitoring. The goal is to help automate monitoring patient finger-hand function between office visits from a remote location, on a smaller scale and with minimal supervision. The contributions presented in this work include development of a detailed set of digital features derived from a custom built gaming platform to highlight client movement performance and algorithms to extract hand structure to approximate goniometry measurements of joint angles monitoring for potential changes during progression of the disease. The significance of this contribution is that it provides a readily accessible, experimental platform for the provision of physical therapy tailored to the individual RA patient through the use of a telerehabilitation gaming platform.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".