Grant: Collaborative Research and Development Grants

Computer modeling and simulation of the human body is rapidly becoming a critical and central tool in a broad range of disciplines, including engineering, education, entertainment, physiology and medicine. Detailed geometric models of human anatomy are available for use as instructional aids and providing realistic computer character animation. Dynamic musculoskeletal models are employed in activities ranging from ergonomic analysis to medical research and treatments. Deformable tissue models are used for surgical simulation, and increasingly for other applications as their fidelity improves. This proposal aims to advance this technology by initiating the development of a dynamic, parametric, multiscale model of human musculoskeletal anatomy that can later be extended to include organ structures and other subsystems. Our industrial partner, Autodesk Research, anticipates using this as the basis for dynamic human models to be integrated into Autodesk's suite of computer products (e.g., Maya and AutoCAD) for a broad range of potential applications including ergonomic studies, advanced animation, and medical training and simulation. Our proposed model will enhance the state-of-the-art in several ways. First, it will provide very high accuracy, including high-resolution bone surfaces and detailed representations of muscle fibre structure and pennation. Second, whereas present commercial musculoskeletal modeling systems generally use point-to-point muscle models, ours will provide 3D finite element (FEM) muscle models coupled with multibody simulation techniques including contact handling and constraints. Third, the model will be multiscale, with users able to select components from a variety of resolutions to allow trade-offs between accuracy and computational speed. Fourth, the model will be parametric, allowing users to adjust its morphological characteristics by adjusting parameters. Finally, it will be designed as a template that can be registered, in whole or in part, onto subject data to create subject-specific models.