PIF2025 - Digital Twins for Neurodegenerative Motor Disorders: The Case of Parkinson’s Disease - (PID2024-162745OB-I00)

Neurodegenerative diseases such as Parkinson’s disease (PD) produce a wide range of motor alterations that are difficult to capture and quantify in clinical practice. This PhD project aims to create a digital twin of PD - an enriched and interactive 3D representation of the patient’s motor behaviour - which can be used by clinicians to better identify, understand and predict critical symptoms. Key Objectives: - Develop a 3D digital twin framework capable of reconstructing and visualizing Parkinsonian motor patterns using wearable and motion capture data. - Implement algorithms for automated movement quantification, extracting spatiotemporal and kinematic biomarkers of motor dysfunction. - Integrate explainable AI methods to reveal which features of movement contribute most to disease characterization and progression tracking. - Combine data-driven and biomechanical models to ensure physiological interpretability and personalization. - Validate the digital twin against clinical assessments and expert annotations. - Demonstrate the potential of the system for clinical decision support, therapy evaluation, and digital phenotyping.