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

• Published in: Frontiers in psychology Vol. 13; p. 857249
• Main Authors: Mahboobeh, Dunia J, Dias, Sofia B, Khandoker, Ahsan H, Hadjileontiadis, Leontios J
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 17.03.2022
• Subjects: i-PROGNOSIS; intelligent Motor Assessment Tests (iMAT); machine learning (KNN SVM RF); Parkinson's Disease (PD); PD staging; Personalized Serious Game Suite (PGS); Psychology; Parkinson's Disease (PD); intelligent Motor Assessment Tests (iMAT); PD staging; i-PROGNOSIS; machine learning (KNN SVM RF); Personalized Serious Game Suite (PGS)
• DOI: 10.3389/fpsyg.2022.857249
• ISSN: 1664-1078

Neurodegenerative Parkinson's Disease (PD) is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of a 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 the status of patients with PD 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 patients with PD 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 their combination, after also taking into consideration the age of patients with PD. These FVs are fed into three machine learning classifiers, i.e., K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF), to infer the stage of each patient with PD. 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 motor skill status of patients with PD and further potentiating PGS/iMAT enhancement with a machine learning a part to infer for the stage of patients with PD. Clearly, this integrated approach provides new opportunities for remote monitoring of the stage of patients with PD, contributing to a more efficient organization and set up of personalized interventions.