Browsing by Author "Marcelino, Isaac Van-Deste"
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- Integration of heterogeneous components for dynamic adaptation of rehabilitation simulationsPublication . Marcelino, Isaac Van-Deste; Lopes, Rui PedroThis dissertation investigates the integration of digital health technologies, artificial intelligence, and immersive environments to design a rehabilitation system for individuals who suffers from schizophrenia disease. Schizophrenia is a multifaceted complex mental disorder, that often leads to loss of physical and cognitive skills, reducing social interaction, with a high relapse rates, that turns rehabilitation a long-term challenge. Virtual Reality has been emerging as a promising tool for rehabilitation, sice it provides a safe, controlled, and motivating therapeutic scenarios. Upon this, the present work introduces a VR-based serious game capable of dynamically adapting its difficulty in response to physiological and behavioural signals. To achieve this solution, several different data from the patient, such as heart rate, body motion rate, and facial expression recognition were continuously monitored and processed to infer the patient’s stress levels in real time. The stress estimate is then integrated into a Dynamic Difficulty Adjustment mechanism, formulated in a Reinforcement Learning algorithm. A Deep Q-Network agent was trained within a simulated environment to learn optimal policies that balance challenge and engagement, ensuring that rehabilitation sessions remain neither frustrating nor trivial. Then, a distributed, event-driven architecture was designed to support the integration of heterogeneous modules, including the Virtual Reality application, a web dashboard, the dynamic difficulty system and a back office server, by using publish-subscribe communication, with Apache ActiveMQ Artemis, and containerized deployment, using Docker, to ensure scalability, modularity, and real-time interoperability. Experimental validation showed that the agent successfully learned to maintain stable levels of difficulty and stress, confirming the feasibility of adaptive rehabilitation driven by physiological signals. Overall, this dissertation demonstrates how the convergence of VR, distributed systems, and artificial intelligence can be harnessed to create adaptive rehabilitation tools that are not only technically robust but also clinically meaningful. The framework provides a foundation for future clinical studies, offering potential applications beyond schizophrenia, including in neurological rehabilitation, stress management, and personalized digital health therapies.