Profiling finger-hand function of rheumatoid arthritis patients using a telerehabilitation gaming system
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.
Telerehabilitation, Rheumatoid-arthritis, Hands, Goniometry