Biological Digital Twins for Elite Athletes

Project description

This project explores the development of a Biological Digital Twin (BDT), a digital representation of an elite athlete that models their biological systems in real time. The aim is to create a data-driven framework capable of capturing the complex interactions between physiological, biomechanical, and behavioural aspects of human performance. The BDT integrates multiple layers of biological information (e.g., cardiovascular activity, muscle dynamics, mental state, and recovery patterns) into a unified digital model. This model evolves continuously using data from wearable sensors and performance tracking systems, enabling a dynamic and personalised representation of each athlete.

Co-supervisors

Dr Leelalanga Seneviratne (Behavioral Scientist), Faculty of Information Technology, University of Moratuwa, Sri Lanka.

Further information

What You Will Work On Students involved in this project may contribute to:

• Designing the architecture of the Biological Digital Twin, including laminar (layered) biological models
• Integrating and analysing data from wearable devices and sensors
• Modelling complex biological states and behaviours such as fatigue, recovery, and performance
• Developing methods for handling incomplete or missing data in dynamic environments
• Building predictive models to simulate athlete states across past, present, and future timelines
• Exploring context-aware modelling, incorporating factors such as game conditions and environment
• Creating visualisations and dashboards to represent the digital twin and its insights

Why This Project Matters This project sits at the intersection of data engineering, artificial intelligence, and sports science. Unlike traditional sports analytics, which focus mainly on performance metrics, this work aims to model the underlying biological processes that drive performance.

• The outcomes of this project have the potential to support
• Personalised training and performance optimisation
• Injury risk prediction and prevention
• Real-time decision-making in competitive environments
• Advanced sports intelligence for coaches, physicians, and team managers

Assumed knowledge

Programming in Python, Training AI/ML models, Data Engineering

Note: You need to register interest in projects from different supervisors (not a number of projects with the one supervisor).
You must also contact each supervisor directly to discuss both the project details and your suitability to undertake the project.