Multibody Dynamics and Predictive Simulation of Human Movements

多体动力学和人体运动的预测模拟

• 批准号: RGPIN-2022-03676
• 负责人: McPhee, John
• 金额: $ 5.54万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765915
• 关键词: Multibody; Dynamics; Predictive; Simulation; Human

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. Above all, the simulations should be able to predict human motions before treatments, without relying on post-treatment experimental data. The US Food and Drug Administration has recently embraced this idea and recommended the use of computer simulations to design new medical devices. However, there are no computer simulations that can quickly and accurately predict three-dimensional (3D) motions of a human, and the corresponding muscle, joint, and contact loads. Therefore, the goal of this research is to invent new methods for predictive "what-if" simulations of complex human motions, including 3D arm-reaching, walking, and running. To achieve this ambitious goal, the applicant will build upon his previous work in multibody biomechanics. 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, including: human joints have complex geometries that cannot be represented by simple mechanical joints; human tissue has very nonlinear properties, especially during contact with hard surfaces; bone, joint, and muscle model parameters are difficult to obtain for individual subjects; the optimization criteria used by our brain and nervous system to coordinate our movements is not well understood. To develop fast and accurate predictive simulations in this research, new subject-specific models of muscles, joints, contact dynamics, and human motion controllers will be incorporated into the applicant's multibody models and simulations. Symbolic computing will be used so that the system equations can be viewed, shared, and automatically converted into highly-optimized simulation code. Symbolic derivatives will support sensitivity analyses and the identification of subject-specific model parameters, and will accelerate the convergence of the underlying optimization methods that drive the predictive dynamic simulations. Quick and reliable predictions of human motions and loads will fuel an explosive growth in biomechanics applications, including assistive and rehabilitation devices, personalized treatments, and wearable technologies. The requested funding will support 7 graduate students and 10 undergraduate researchers who will become the next generation of Canadian innovators in these exciting new areas.

一位外科医生有一个新的想法，可以帮助受伤的病人再次行走。手术包括重新安排一些肌腱和韧带。但是，外科医生在将其用于患者之前如何测试他们的想法？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。 最重要的是，模拟应该能够预测治疗前的人体运动，而不依赖于治疗后的实验数据。美国食品和药物管理局最近接受了这一想法，并建议使用计算机模拟来设计新的医疗设备。然而，没有计算机模拟可以快速准确地预测人体的三维（3D）运动以及相应的肌肉、关节和接触载荷。因此，这项研究的目标是发明新的方法来预测复杂的人体运动，包括3D手臂伸展，行走和跑步。为了实现这一雄心勃勃的目标，申请人将建立在他以前的工作在多体生物力学。 使用“假设”仿真，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，航空航天和汽车工业广泛采用。然而，最近试图将多体动力学扩展到3D人体运动的独特特征的尝试遇到了许多挑战，包括：人体关节具有复杂的几何形状，不能由简单的机械关节表示;人体组织具有非常非线性的特性，特别是在与硬表面接触期间;骨骼、关节和肌肉模型参数难以获得个体受试者;我们的大脑和神经系统用来协调我们的运动的优化标准还没有很好地理解。 为了在这项研究中开发快速准确的预测模拟，新的肌肉，关节，接触动力学和人体运动控制器的特定对象模型将被纳入申请人的多体模型和模拟。将使用符号计算，以便系统方程可以查看，共享，并自动转换为高度优化的仿真代码。符号导数将支持敏感性分析和特定对象模型参数的识别，并将加速驱动预测动态模拟的底层优化方法的收敛。 对人体运动和负荷的快速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、个性化治疗和可穿戴技术。申请的资金将支持7名研究生和10名本科生研究人员，他们将成为这些令人兴奋的新领域的下一代加拿大创新者。