Dias, Sofia B.Sofia B.DiasGrammatikopoulou, AthinaAthinaGrammatikopoulouGrammalidis, NikosNikosGrammalidisDiniz, José A.José A.DinizSavvidis, TheodoreTheodoreSavvidisKonstantinidis, EvdokimosEvdokimosKonstantinidisBamidis, PanagiotisPanagiotisBamidisStadtschnitzer, MichaelMichaelStadtschnitzerTrivedi, DhavalDhavalTrivediKlingelhoefer, LisaLisaKlingelhoeferKatsarou, ZoeZoeKatsarouBostantzopoulou, SevastiSevastiBostantzopoulouDimitropoulos, KosmasKosmasDimitropoulosHadjileontiadis, Leontios J.Leontios J.Hadjileontiadis2022-03-142022-03-142020https://publica.fraunhofer.de/handle/publica/40963310.1109/ICIP40778.2020.9191017The primary manifestations of Parkinson Disease (PD) concern abnormalities of movement associated with the constant deterioration of motor skills. Such motor impairment affects patients movement accuracy and coordination, disrupting their daily life. Taking into account recent studies stating that computer-based physical therapy games can be used as a PD rehabilitation option, we propose a novel Exergame, the iPrognosis Warming up Game (http://www.i-prognosis.eu/), as a user-friendly tool that could both serve as a computer-based physical therapy game, as well as a means of accurately and automatically identifying the severity of PD motor symptoms. To this regard, we propose a novel deep learning methodology for motor impairment stage prediction that relies solely on human body motion data extracted from the recorded game sessions. Experimental results using a dataset of both early and advanced PD patients reveal a good classification performance of the proposed methodology, predicting the motor impairment stage of PD patients and paving the way for additional research in the field.encamerasParkinson's diseasegamesphysicsIndexesPredictive modelsi-PROGNOSISiPrognosis Personalized Game SuiteParkinsons DiseaseMotor Statusdeep learning005006629conference paper