A framework for automatic hand range of motion evaluation of rheumatoid arthritis patients

We propose a framework for evaluation of finger movement patterns on Rheumatoid Arthritis patients: flexion, extension, abduction and adduction. The framework uses a state-of-the-art 3D hand pose estimation method that runs in real-time, allowing users to visualize 3D skeleton tracking results at the same time as the depth images are acquired. We compute flexion and abduction angles from the obtained skeleton pose parameters. We performed data acquisition from a cohort of patients and a control set and compared the angles from those two sets of people. An analysis using time series similarity with frequency domain descriptors is adopted to characterize the movement patterns for flexion/extension. We performed classification experiments using these descriptors, thus distinguishing movement sequences of hands with rheumatoid arthritis from healthy hands. The descriptors used in the classification experiment were effective and reached average results of 89% in scenarios of unseen subjects, and an average of 82% in experiments with sample synthesis that allow a more robust statistical performance evaluation. Our framework allows the characterization of the current state of the disorder in each patient, with minimal intervention and reduced evaluation time.