Digital Twin of the Musculoskeletal System
Musculoskeletal (MSK) models represent the dynamics of the human body and can output many different variables i.e. joint angles, joint moments and muscle force. Personalised movement predictions provide accurate outcome variables than a generic prediction. Therefore, we would like to develop a digital twin of the MSK system, which can then be used for personalised movement predictions. Image-based personalisation is the state-of-the-art. Anthropometric variables, such as bone geometry and muscle attachment points can be derived from magnetic resonance imaging (MRI). Muscle parameters require diffusion tensor imaging (DTI) to visualise the alignment of fibres, which is important for the derivation of the muscle size as well as the fibre length. The goal of this project is to develop a personalised digital twin of the MSK system using DTI measurements, and investigate if such a digital twin can improve accuracy of movement predictions. The aim is to also investigate to what extent image processing can be automated. Furthermore, identification of groups using the personalised models, e.g. to detect MSK diseases, such as rheumatoid arthritis will be investigated.