Motion analysis of Parkinson diseased patients using a video game approach

Abstract

Parkinson's disease (PD) is a progressive neurological disorder and the second most common age-related neurodegenerative disease after Alzheimer's disease. The primary symptoms of the disease are associated with the loss of motor skills affecting patients' movement and coordination and disrupting their daily life. Unfortunately, such motor symptoms cannot be fully relieved by therapeutic options. On the other hand, studies have shown that regular training and exercising can prove neuroprotective in PD patients helping them maintain independent longer. Based on recent studies stating that computer-based physical therapy games can be used as an option for facilitating PD rehabilitation exercise programs, we present the development of a body motion based videogame, using the Kinect sensor, targeted for PD patients. We tested twelve patients with advanced forms of PD motor symptoms (UPDRS motor score>20) and six initial stage PD patients (UPDRS motor score<20). All participants underwent an (UPDRS) motor skills pretest and afterwards performed three training sessions. In this paper, we will present part of our research aiming to analyze the movement patterns of PD patients in order to detect statistical significant differences between groups of different impairment level based on their UPDRS motor score and their performance. Consequently, we adopt a deep learning approach by analyzing the recorded human skeleton sequences for predicting the players' level of motor skills decline. Such methods and data can serve as preliminary evidence for further larger and controlled studies to propose such an exergame that can independently detect and adapt its difficulty level to better match players' ability providing a more targeted and personalized rehabilitation option.