Predicting Freezing of Gait in Parkinson’s Disease: A Machine Learning-Based Approach in ON and OFF Medication States

Research overview

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Freezing of Gait (FoG) is a debilitating motor symptom of Parkinson’s Disease (PD), characterized by sudden episodes where patients struggle to initiate or sustain movement, often describing a sensation of their feet being “glued to the ground.” This study investigates the potential of machine learning (ML) models to predict FoG severity in PD patients, focusing on the influence of dopaminergic medication by comparing gait parameters in ON and OFF medication states. Specifically, we employ spatiotemporal gait features to develop a predictive model for FoG episodes, leveraging a Random Forest Regressor to identify the most influential gait parameters associated with FoG severity in each medication state. The results indicate that the model achieved higher predictive performance in the OFF-medication condition (R² = 0.82, MAE = 2.25, MSE = 15.23) compared to the ON-medication condition (R² = 0.52, MAE = 4.16, MSE = 42.00). These findings suggest that dopaminergic treatment alters gait dynamics, potentially reducing the reliability of FoG predictions when patients are medicated. Feature importance analysis further highlights distinct gait characteristics that contribute to FoG prediction depending on medication status. In the OFF condition, step length asymmetry and stride variability emerged as dominant predictors, while in the ON condition, changes in gait rhythm and stepping frequency became more prominent, reflecting medication-induced compensatory adjustments. These findings underscore the need for predictive models that account for medication-induced gait variability, ensuring more reliable FoG detection. By integrating spatiotemporal gait analysis and ML-based prediction, this study contributes to the development of personalized intervention strategies for PD patients experiencing freezing episodes.

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