Multibody Dynamics, Predictive Simulation, and Model-based Control of Biomechanical Systems

生物力学系统的多体动力学、预测仿真和基于模型的控制

• 批准号: RGPIN-2016-04332
• 负责人: McPhee, John
• 金额: $ 5.52万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709166
• 关键词: Multibody; Dynamics; Predictive; Simulation; Model

A surgeon has a new idea for a procedure that may help an injured patient walk again. The procedure involves re-routing some tendons and ligaments. But how does the surgeon test out their idea before using it on a patient? How can an engineer design and test a new prosthesis or rehabilitation device, and have confidence in its performance and safety? Computer simulations can help answer these and other questions, but the simulations must be accurate and fast. The goal of this research is to invent new methods to simulate the incredible complexity of human motions, including three-dimensional (3D) arm-reaching, walking, and running. The simulations should also predict the forces in muscles and joints. To achieve this ambitious goal, the applicant will build upon his previous work in multibody dynamics, in which algorithms have been created to automatically simulate the motion of a system of interconnected bodies. Using “what-if” simulations, new designs for products and processes can be quickly evaluated; hence, multibody dynamics has been widely adopted by the robotics, aerospace, and automotive industries. However, recent attempts to extend multibody dynamics to the unique features of 3D human motions have encountered a number of challenges: one must coordinate the multiple muscles that control a single joint; these muscles will change directions as they wrap over multiple surfaces while stretching and contracting; human tissue shows very nonlinear properties, particularly during contact with the ground; human joints have complex geometries that cannot be represented by simple mechanical joints; the criteria used by our brain and nervous system to coordinate our movements is not well understood. Above all, the simulations should be “predictive” and not require experimental data from humans as inputs. Currently, there are no multibody dynamics algorithms that can quickly and accurately predict the 3D motion of a human, and the corresponding muscle, joint, and contact forces. To develop such algorithms, new mathematical models of muscles, joints, 3D foot-ground contacts, and human motion controllers will be incorporated into the applicant's multibody theories. Advanced symbolic computing will be used so that the system equations can be viewed and shared with other researchers, unlike conventional numerical approaches to dynamics. More importantly, symbolic computing will help us to create optimized simulation code that runs many times faster than these conventional methods. Super-fast and reliable predictions of human motions and forces will fuel an explosive growth in biomechanics applications, including assistive and rehabilitation devices, exoskeletons, and wearable technologies. The requested funding will support 7 graduate students and 10 undergraduate researchers who will develop the skills needed to become the next generation of innovators in these exciting new areas.

一位外科医生有了一个新想法，可以帮助受伤的病人重新行走。这个过程包括重新安排一些肌腱和韧带。但是，外科医生在将其应用于患者之前如何测试他们的想法呢？工程师如何设计和测试新的假肢或康复装置，并对其性能和安全性有信心？计算机模拟可以帮助回答这些和其他问题，但模拟必须准确和快速。