Freezing of Gait Prediction using Proactive Agent that Learns from Selected Experience and DDQN Algorithm

TL;DR

This paper introduces a reinforcement learning framework using a DDQN with prioritized experience replay to predict Freezing of Gait episodes in Parkinson's Disease patients, enabling proactive interventions with extended prediction horizons.

Contribution

It presents a novel RL-based approach with a DDQN architecture and experience replay for early FOG prediction, improving prediction accuracy and horizon over previous methods.

Findings

Achieved up to 8.72 seconds prediction horizon before FOG onset.

Demonstrated robustness in both subject-dependent and independent evaluations.

Enhanced prediction performance using prioritized experience replay.

Abstract

Freezing of Gait (FOG) is a debilitating motor symptom commonly experienced by individuals with Parkinson's Disease (PD) which often leads to falls and reduced mobility. Timely and accurate prediction of FOG episodes is essential for enabling proactive interventions through assistive technologies. This study presents a reinforcement learning-based framework designed to identify optimal pre-FOG onset points, thereby extending the prediction horizon for anticipatory cueing systems. The model implements a Double Deep Q-Network (DDQN) architecture enhanced with Prioritized Experience Replay (PER) allowing the agent to focus learning on high-impact experiences and refine its policy. Trained over 9000 episodes with a reward shaping strategy that promotes cautious decision-making, the agent demonstrated robust performance in both subject-dependent and subject-independent evaluations. The model achieved a prediction horizon of up to 8.72 seconds prior to FOG onset in subject-independent scenarios and 7.89 seconds in subject-dependent settings. These results highlight the model's potential for integration into wearable assistive devices, offering timely and personalized interventions to mitigate FOG in PD patients.