10184975Enhancing Clinical Efficiency Through AI-Driven ROM Assessment for Stroke PatientsActiveCollaborative R&DInnovate UKStroke rehabilitation continues to rely on manual range-of-motion (ROM) measurement using handheld goniometers. These methods suffer from ±6--10° inter-rater variability, consume significant therapist time, and produce documentation that is inconsistent, subjective, and not aligned with evolving audit and reimbursement needs. Therapist shortages across over 85% of NHS Trusts further amplify these inefficiencies, with up to 30--40% of clinical time lost to manual scoring and documentation. eXRt has already developed a Proof of concept clinical measurement platform capable of ±2° motion-tracking accuracy, powered by a computer-vision engine. While the initial ROM prototype demonstrates strong technical feasibility, further development, validation, workflow adaptation, and local integration are required to reach TRL7 and support regional adoption. This NI Launchpad project focuses specifically on completing the development of the automated goniometry module, validating it clinically within Northern Ireland, and embedding the technology into the regional life & health sciences cluster through collaboration with HIRANI, NI Trusts, Queens University, and digital-health SMEs.