Freezing of Gait Prediction using Proactive Agent that Learns from Selected Experience and DDQN Algorithm
arXiv:2603.03651v1 Announce Type: new 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
arXiv:2603.03651v1 Announce Type: new 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.
Executive Summary
This study presents a reinforcement learning-based framework for predicting Freezing of Gait (FOG) in individuals with Parkinson's Disease (PD). The model, utilizing a Double Deep Q-Network (DDQN) architecture with Prioritized Experience Replay (PER), achieved a prediction horizon of up to 8.72 seconds prior to FOG onset in subject-independent scenarios. The results demonstrate the model's potential for integration into wearable assistive devices, offering timely and personalized interventions to mitigate FOG in PD patients. The study's findings have significant implications for the development of assistive technologies and may lead to improved quality of life for individuals with PD.
Key Points
- ▸ The model utilizes a reinforcement learning-based framework to predict FOG episodes in PD patients.
- ▸ The DDQN architecture with PER enables the agent to focus learning on high-impact experiences and refine its policy.
- ▸ The model achieved a prediction horizon of up to 8.72 seconds prior to FOG onset in subject-independent scenarios.
Merits
Strength in Model Design
The DDQN architecture with PER is a novel and effective approach for predicting FOG episodes, allowing the agent to focus on high-impact experiences and refine its policy.
Demerits
Limitation in Generalizability
The model's performance is dependent on the specific data used for training, and further research is needed to determine its generalizability to diverse patient populations.
Expert Commentary
This study makes a significant contribution to the field of assistive technologies for PD patients. The use of a reinforcement learning-based framework and the DDQN architecture with PER is a novel and effective approach for predicting FOG episodes. The model's performance is impressive, with a prediction horizon of up to 8.72 seconds prior to FOG onset in subject-independent scenarios. However, further research is needed to determine the model's generalizability to diverse patient populations. The study's findings have significant implications for the development of assistive technologies and may lead to improved quality of life for individuals with PD.
Recommendations
- ✓ Future research should focus on determining the model's generalizability to diverse patient populations and exploring its integration into wearable assistive devices.
- ✓ Investment in the development of assistive technologies for PD patients should be prioritized, with a focus on improving mobility and reducing falls.