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

一位外科医生有了一个新的想法，可以帮助受伤的病人重新行走。手术包括对一些肌腱和韧带进行重新布线。但在将其用于病人之前，外科医生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设备，并对其性能和安全性充满信心？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。 这项研究的目标是发明新的方法来模拟令人难以置信的复杂的人体运动，包括三维(3D)手臂伸展、行走和跑步。这些模拟还应该预测肌肉和关节中的力。为了实现这一雄心勃勃的目标，申请者将在他之前在多体动力学方面的工作的基础上再接再厉，在多体动力学中，已经创建了自动模拟相互关联的物体系统的运动的算法。 使用“假设”模拟，可以快速评估产品和工艺的新设计；因此，多体动力学已被机器人、航空航天和汽车行业广泛采用。然而，最近将多体动力学扩展到三维人体运动的独特功能的尝试遇到了一些挑战：一个人必须协调控制单个关节的多个肌肉；这些肌肉在拉伸和收缩时会在多个表面上包裹时会改变方向；人体组织显示出非常非线性的特性，特别是在与地面接触的过程中；人类关节具有复杂的几何形状，无法用简单的机械关节来表示；我们的大脑和神经系统用来协调我们运动的标准还没有被很好地理解。 最重要的是，模拟应该是“预测性的”，不需要人类的实验数据作为输入。目前，还没有多体动力学算法可以快速准确地预测人体的3D运动以及相应的肌肉、关节和接触力。 为了开发这样的算法，新的肌肉、关节、3D脚底接触和人体运动控制器的数学模型将被纳入申请者的多体理论。与传统的动力学数值方法不同，将使用先进的符号计算，以便可以查看系统方程并与其他研究人员共享。更重要的是，符号计算将帮助我们创建优化的模拟代码，其运行速度比这些传统方法快许多倍。 对人体运动和力量的超快和可靠预测将推动生物力学应用的爆炸性增长，包括辅助和康复设备、外骨骼和可穿戴技术。申请的资金将支持7名研究生和10名本科生，他们将发展成为这些令人兴奋的新领域的下一代创新者所需的技能。

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.