// AI & data-driven technologies
Rehabilitation Technology:
Develop a tool that collects and interprets disparate, measurable data using artifical intelligence to help clinical staff deliver personalised rehabilitation and track patient outcomes
RehabAI: Towards adaptive, data-driven rehabilitation planning
Our Team
Isibor Kennedy Ihianle
Nottingham Trent University
Contact: isibor.ihianle@ntu.ac.uk
Ahmad Lotfi
Nottingham Trent University
Pedro Machado
Nottingham Trent University
Everistus Nwogo
Nottingham Trent University
What is the problem that needs solving?
Rehabilitation planning for stroke, brain injury, and other neurological conditions is complex, resource-intensive, and often relies on generalised guidelines rather than personalised strategies. This creates an unmet need for intelligent tools that can account for the diverse clinical, psychological, and social factors that shape recovery. This project addresses that need by developing an advanced decision-support system that uses AI to recommend tailored rehabilitation plans. Unlike existing tools, our approach goes beyond clinical data integrating data-driven modelling with behavioural economics to balance clinical outcomes, patient experience, and resource implications.
How can rehabtech address this problem?
We have already built a proof-of-concept prototype that demonstrates personalised recommendations using synthetic patient data. This project will extend the system’s capabilities, testing its feasibility as a strategic planning tool for both individual patient journeys and wider service level forecasting. The expected outcome is a robust prototype that demonstrates technical feasibility and translational potential, laying the foundation for clinical testing and eventual NHS integration. By enabling personalised care and more efficient resource planning, this project aligns closely with EMERGE RehabTech’s vision of transforming rehabilitation through technology, driving improvements in patient outcomes and service efficiency across the UK.