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Motion Analysis on Depth Camera Data to Quantify Parkinson's Disease Patients' Motor Status within the Framework of I-Prognosis Personalized Game Suite

: Dias, Sofia B.; Grammatikopoulou, Athina; Grammalidis, Nikos; Diniz, José A.; Savvidis, Theodore; Konstantinidis, Evdokimos; Bamidis, Panagiotis; Stadtschnitzer, Michael; Trivedi, Dhaval; Klingelhoefer, Lisa; Katsarou, Zoe; Bostantzopoulou, Sevasti; Dimitropoulos, Kosmas; Hadjileontiadis, Leontios J.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
IEEE International Conference on Image Processing, ICIP 2020. Proceedings : September 25-28, 2020 Virtual Conference, Abu Dhabi, United Arab Emirates
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-6395-6
ISBN: 978-1-7281-6394-9
ISBN: 978-1-7281-6396-3
International Conference on Image Processing (ICIP) <2020, Online>
European Commission EC
H2020; 690494; i-Prognosis
Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS
Fraunhofer IAIS ()
cameras; Parkinson's disease; games; physics; Indexes; Predictive models; i-PROGNOSIS; iPrognosis Personalized Game Suite; Parkinsons Disease; Motor Status; deep learning

The 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 (, 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.