CAREER: Scalable Musculoskeletal Simulation for Biomechanical Animation

职业：生物力学动画的可扩展肌肉骨骼模拟

• 批准号: 1846368
• 负责人: Shinjiro Sueda
• 金额: $ 55万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846368&HistoricalAwards=false
• 关键词: CAREER; Scalable; Musculoskeletal; Simulation; Biomechanical

This project will build new computational models for biomechanically based animation of digital humans, suitable for a wide range of applications. Beyond computer animation, the result of this research will be used for anatomy training and education by combining musculoskeletal simulations with haptic feedback. This research will also lead to novel applications in diverse other fields, including: medicine, for surgical training and stroke rehabilitation; ergonomics, for better understanding of energy use; bio-paleontology, for discovering the movement of extinct animals; robotics, for bio-inspired tendon-driven actuators; and neuroscience, for reverse engineering the brain by providing a computational testbed for understanding motor control. The integrated educational objective of this project is to use concepts from computer graphics, computer animation, and computational biomechanics as motivational tools to teach people of diverse ages and backgrounds about STEM careers.This research will build on existing musculoskeletal simulations in biomechanics and graphics. In recent decades, two contrasting methods for musculoskeletal simulation have been developed: line-based methods and volume-based methods. Unfortunately, there is currently no way to get the best of both worlds because these methods are built on fundamentally different principles; line-based methods are built on rigid body dynamics, whereas volume-based methods are built on continuum mechanics. This project will bridge the gap between these two approaches. The specific research objectives are threefold. (1) To imbue the standard line-based methods with two important features, muscle inertia and branching. The guiding principle for this research objective is to expand the current capabilities of existing line-based methods while enabling easy integration into off-the-shelf simulators. (2) To apply intelligent model reduction that is specific to muscles, in order to make the volume-based methods more efficient and robust, and capable of gracefully degrading to line-based methods. (3) To validate the proposed computational models rigorously with real-world anatomical data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

本项目将为基于生物力学的数字人类动画建立新的计算模型，适用于广泛的应用。除了计算机动画之外，这项研究的结果将通过将肌肉骨骼模拟与触觉反馈相结合，用于解剖学训练和教育。这项研究还将在其他不同领域带来新的应用，包括：医学，外科训练和中风康复；人体工程学，以更好地了解能源使用；生物古生物学，发现灭绝动物的活动；机器人技术，用于仿生肌腱驱动驱动器；神经科学，通过为理解运动控制提供一个计算测试平台来逆向工程大脑。该项目的综合教育目标是利用计算机图形学、计算机动画和计算生物力学的概念作为激励工具，向不同年龄和背景的人们传授STEM职业。这项研究将建立在生物力学和图形学中现有的肌肉骨骼模拟的基础上。近几十年来，肌肉骨骼模拟有两种截然不同的方法：基于线的方法和基于体的方法。不幸的是，目前还没有办法做到两全其美，因为这些方法建立在根本不同的原则之上；基于线的方法建立在刚体动力学基础上，而基于体的方法建立在连续介质力学基础上。这个项目将弥合这两种方法之间的差距。具体的研究目标有三个方面。(1)赋予基于标准线的方法两个重要特征：肌肉惯性和分支。本研究目标的指导原则是扩展现有基于线的方法的当前能力，同时使其易于集成到现成的模拟器中。(2)应用针对肌肉的智能模型约简，使基于体积的方法更加高效和鲁棒，并能够优雅地退化到基于线的方法。(3)用真实解剖数据严格验证所提出的计算模型。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

NBD-Tree: Neural Bounded Deformation Tree for Collision Culling of Deformable Objects

NBD-Tree：用于可变形物体碰撞剔除的神经有界变形树

• 发表时间: 2023
• 期刊: The Southwest Data Science Conference
• 作者: Zesch, Ryan S.;Witemeyer, Bethany R.;Xiong, Ziyan;Levin, David I.W.;Sueda, Shinjiro
• 通讯作者: Sueda, Shinjiro

QLB: Collision-Aware Quasi-Newton Solver with Cholesky and L-BFGS for Nonlinear Time Integration

QLB：具有 Cholesky 和 ​​L-BFGS 的碰撞感知拟牛顿求解器，用于非线性时间积分

• DOI: 10.1145/3487983.3488297
• 发表时间: 2021
• 期刊: Interaction and Games
• 作者: Witemeyer, Bethany;Weidner, Nicholas J.;Davis, Timothy A.;Kim, Theodore;Sueda, Shinjiro
• 通讯作者: Sueda, Shinjiro

Computation of Filament Winding Paths with Concavities and Friction

• DOI: 10.1016/j.cad.2021.103089
• 发表时间: 2021-12
• 期刊: Comput. Aided Des.
• 作者: Hang Li;S. Sueda;J. Keyser

Efficient Tactile Simulation with Differentiability for Robotic Manipulation

• 发表时间: 2022
• 作者: Jie Xu;Sangwoon Kim;Tao Chen;Alberto Rodriguez;Pulkit Agrawal;W. Matusik;S. Sueda

Muscle Inertia During Running: A Massive Change of Moments?

跑步时的肌肉惯性：时刻的巨大变化？

• 发表时间: 2022
• 期刊: 40th International Society of Biomechanics in Sports Conference
• 作者: Verheul, Jasper;Sueda, Shinjiro;Yeo, Sang Hoon
• 通讯作者: Yeo, Sang Hoon