AUTHOR=Mahboobeh Dunia J. , Dias Sofia B. , Khandoker Ahsan H. , Hadjileontiadis Leontios J. 

TITLE=Machine Learning-Based Analysis of Digital Movement Assessment and ExerGame Scores for Parkinson's Disease Severity Estimation

JOURNAL=Frontiers in Psychology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.857249

DOI=10.3389/fpsyg.2022.857249

ISSN=1664-1078

ABSTRACT=The neurodegenerative Parkinson’s Disease (PD) is one of the common incurable diseases amongst the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of patient’s status can be subjective to physicians’ experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing PD patients' status can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite (PGS) and intelligent Motor Assessment Tests (iMAT), produced within the i-PROGNOSIS European project (www.i-prognosis.eu), are explored in the current study. More specifically, data from 27 PD patients at Stage 1 (9) and Stage 3 (18) produced from their interaction with PGS/iMAT are analyzed. Five feature vector (FV) scenarios are set, including features from PGS or iMAT scores or from their combination, after also taking into consideration the PD patients' age. These FVs are fed into three machine learning classifiers, i.e.,  K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest (RF), to infer for the stage of each PD patient. A Leave-One-Out Cross-Validation (LOOCV) method is adopted for testing the classification performance. The experimental results show that a high (>90%) classification accuracy is achieved from both data sources (PGS/iMAT), justifying the effectiveness of PGS/iMAT to efficiently reflect the PD patients' motor skills status and further potentiating PGS/iMAT enhancement with a machine learning part to infer for the PD patient's stage. Clearly, this integrated approach provides new opportunities for remote monitoring of the PD patients' stage, contributing to a more efficient organization and set up of personalized interventions.