Integrating Triaxial IMU Sensors and Ensemble Learning for Effective Parkinson Disease Severity Classification

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Abstract Parkinson disease (PD) is a progressive neurodegenerative disease that can have a significant impact on motor performance, resulting in the appearance of symptoms such as tremors, rigidity, postural instabilities and bradykinesia. Timely clinical treatment, disease management and quality life of the patients are closely linked to early and appropriate identification of PD. Over the past few years, the growth of wearable sensor technology and artificial intelligence (AI) have made it possible to create non-invasive and data-driven disease detection methods. This paper proposes a comparative system using artificial intelligence to detect Parkinson’s disease by analyzing the motion and tremor data captured by an inertial measurement unit (IMU). The data comprises the signals of the acceleration and gyroscope sensors measuring movement in three directions (X, Y and Z). The signs and symptoms provide helpful information about subtle motor deficits associated with PD.Several classification models like Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were used to compare their effectiveness. The Logistic Regression model had a performance around 75% in all evaluation metrics and K-Nearest Neighbours (KNN) around 90%. The support vector machine (SVM) performed almost 94% whereas the performance of classifiers such as Decision Tree and XGBoost was close to 96% and overall classification efficacy, respectively. LightGBM model performs consistently at the best rank among all of the evaluated methods, having Accuracy, Precision, Recall and F1-score of around 97%. The results show that the proposed machine learning approach offers an accurate and effective predictive capability in the classification of PD severity.

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