Estimation of Posturographic Trajectory Using k-Nearest Neighbors Classifier in Patients with Rheumatoid Arthritis and Osteoarthritis.

Rheumatoid arthritis (RA) and osteoarthritis (OA) are common rheumatic diseases and account for a significant percentage of disability. Posturography is a method that assesses postural stability and quantitatively evaluates postural sways. The objective of this study was to estimate posturographic trajectories applying pattern recognition algorithms. To this end, k-nearest neighbors (k-NN) classifier was used to differentiate between healthy subjects and patients with OA and RA. The following parameters of trajectories were computed: radius of sways, developed area, total length, and two directional components of sways: length of left-right and forward-backward motions. Posturographic tests were applied with eyes open and closed, and with biofeedback control. We found that in RA, the radius of sways, the trajectory area, and the biofeedback coordination were related to the patients' condition. The trajectory dynamics in OA patients were smaller compared to those in RA patients. The smallest misclassification errors were observed after feature selection in the biofeedback test compared with the eyes open and closed tests. We conclude that the estimation of posturographic trajectory with k-NN classifier could be helpful in monitoring the condition of RA patients.