Limb Prescribe: Text-to-Pose Generative Model for Therapeutic Exercise Prescription

Date of Award


Document Type


Degree Name

Master of Science in Computer Vision


Computer Vision

First Advisor

Prof. Mohammad Yaqub

Second Advisor

Prof. Karthik Nandakumar


Despite physical therapy's significant impact on improving quality of life, its high cost often renders it a luxury. This thesis focuses on "Limb Prescribe: Text-to-Pose Generative Model for Therapeutic Exercise Prescription," which aims to make physiotherapy more accessible. Our research began by understanding the domain and identifying areas where AI could add value through multidisciplinary frameworks. We developed Limb Prescribe to address the customization needs of therapeutic exercises. In physiotherapy, each case is unique, with specific needs and constraints. Limb Prescribe is a text-to-pose generative model designed for therapeutic exercise prescriptions. By integrating pose initialization, iterative pose generation based on previous poses, and incorporating physical constraints, we enhance the model's suitability for exercise prescription. Feedback from fifteen physiotherapists confirmed the model's potential, highlighting the need for further development.


Thesis submitted to the Deanship of Graduate and Postdoctoral Studies

In partial fulfilment of the requirements for the M.Sc degree in Computer Vision

Advisors:Hisham Cholakkal, Abdulmotaleb Elsaddik

with 2 years embargo period

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