Automatic classification of idiopathic Parkinson's disease using kinematic data of motion capture systems

Idiopathic Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects the human nervous system. PD is the second most common neurodegenerative disorder worldwide, affecting more than 10 million people. Despite its high prevalence, the diagnosis still relies on subjective assessments that examine motor performance through clinical scales. The use of sensor-based technologies offers a promising opportunity to improve diagnosis processes as it might make the disease progression quantifiable and more objective. This paper presents a methodology for the automatic detection of PD using kinematic data in a novel database that is being recorded for the analysis of this disorder using motion capture technologies. Experiments are carried out with 37 patients with PD and 15 healthy subjects wearing Inertial Measurement Units (IMU) and photogrammetry systems while walking on flat terrain. Five different classification systems, including a novel transformer-based foundational model for tabular data (TabFPN), were used to discriminate between healthy controls and patients with PD and compared to more classical and tabular-based classification algorithms. The results indicate the abilities of TabFPN in automatically discriminating between PD and controls, reaching an accuracy of up to 78% and a ROC-AUC of 89%.