Prediction of Freezing of Gait in Parkinsons Disease using Explainable AI and Federated Deep Learning for Wearable Sensors

This study leverages an Inertial Measurement Unit (IMU) dataset to develop explainable AI methods for the early detection and prediction of Freezing of Gait (FOG), a common symptom in Parkinson's disease. Machine learning models, including CatBoost, XGBoost, and Extra Trees classifiers, are employed to accurately categorize FOG episodes based on relevant clinical features. A Stacking Ensemble model achieves superior performance, surpassing a hybrid bidirectional GRU model and reaching nearly 99% classification accuracy. SHAP interpretability analysis reveals that time (seconds) is the most influential factor in distinguishing gait patterns. Additionally, the proposed FOG prediction framework incorporates federated learning, where models are trained locally on individual devices and aggregated on a central server using a federated averaging approach, utilizing a hybrid Conv1D + LSTM architecture for enhanced predictive capability.