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Cyril J.W Donnelly

Title In-silico Rehabilitation
Authors Cyril J.W Donnelly
Abstract Globally, non-communicable diseases (e.g., osteoarthritis), are costing world health care systems trillions of dollars annually. In parallel, as the global population ages, there is a relatively smaller workforce capable of maintaining current healthcare standards for this growing population. In the past 20 years, there has been a technology revolution with the development of open science, open AI, and the ability to acquire, store and manage vast amounts of clinically relevant information in a cost and time effective manner. The problem now, is how to effectively analyse this mountain of information in a manner that can maintain quality health care to the patient, reduce the stain placed on a relatively shrinking health care workforce, while being cost effective. Arguably, one solution to this multifaceted global problem is technology. The term ‘in-silico Rehabilitation’ was recently coined as a term that allows for the quantitative identification of abnormal human movement, as well as optimal movement prescriptions to these biomechanical abnormalities; which I believe underpins the majority of the top 5 non-communicable diseases globally. For this talk, I will provide an oversight on how an open-source vector field statistics framework called SPM1D (, with and open-source physics based musculoskeletal simulation platform, OpenSim ( can be used by a clinician in the design or monitoring a patient’s rehabilitation journey. Simply, SPM1D identifies where and when, within an individual’s time varying kinematic/kinetic a biomechanical abnormality is observed, and how OpenSim, with a healthy reference dataset can be used to provide optimized movement prescription feedback.