Assessing motor skills in Parkinson's Disease using smartphone-based video analysis and machine learning

Parkinson's disease (PD), the second most prevalent neurodegenerative condition, lacks a cure, but its symptoms can be managed. Its complex diagnosis and assessment need ongoing monitoring, highlighting the potential use of digital assessment tools for enhancing patient management, even outside the clinical settings. In this vein, this paper proposes a smartphone-based video analysis approach for assessing motor skills, particularly balance and posture, in individuals diagnosed with PD. In particular, the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) ratings for items "3.8" (leg agility), "3.9" (arising from chair),"3.13" (posture) and "3.10" (gait) are estimated by capturing and analysing video from PD patients, while performing a Comprehensive Motor Function Test. Specifically, a 3D pose landmark detection (skeleton extraction) model based on the the MediaPipe Machine Learning Platform is used and different motion features are estimated from the captured videos that may correlate with the MDS-UPDRS assessments provided by clinicians. A machine learning pipeline (evaluating five different ML classifiers) is then proposed to examine the feasibility of using these features for monitoring the balance and posture of PD patients. Experimental results, obtained using a cohort of 17 Greek PD patients, voluntarily participating in this study, demonstrate that certain features have significant correlation with the clinical MDS-UPDRS ratings. These promising results showcase the potentiality of digital assessment to provide objective representation of the PD patient's motor skills, supporting both PD clinical assessment and self-management. Ongoing work within the AI-PROGNOSIS project will further validate these findings within a larger cohort and from additional countries.