Toolbox for estimation, simulation and control of multi-joint movements

用于估计、模拟和控制多关节运动的工具箱

• 批准号: 7512485
• 负责人: Emanuel Todorov
• 金额: $ 16.9万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-01 至 2009-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7512485
• 关键词: Academia; Algorithms; Appendix; Arts; Athletic; Automobile Driving; Behavior; Biological; Biomechanics; Brain; Budgets; Calibration; Clinical; Collaborations; Collection; Compatible; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Detection; Development; Devices; Documentation; Eating; Electric Stimulation; Electromagnetics; Ensure; Environment; Equilibrium; Extensible Markup Language; Feedback; Funding; Generations; Goals; Grant; Hour; Human; Human body; Imagery; Industry; Joints; Laws; Left; Libraries; Limb structure; Location; Measurement; Methodology; Methods; Modality; Modeling; Motion; Motor; Movement; Muscle; Numbers; Occupational Safety; Operative Surgical Procedures; Optics; Output; Performance; Placement; Positioning Attribute; Production; Productivity; Publishing; Purpose; Reconstructive Surgical Procedures; Rehabilitation therapy; Reliance; Research; Research Personnel; Running; Sensory Receptors; Signal Transduction; Simulate; Skeletal system; Skeleton; Software Tools; Specific qualifier value; Standards of Weights and Measures; Students; Suggestion; System; Testing; Time; Translating; Trees; United States National Institutes of Health; Variant; Work; Writing; animation; base; clinical application; computer human interaction; data modeling; data structure; design; engineering design; experience; graphical user interface; interest; kinematics; motor control; neural prosthesis; neuroregulation; relating to nervous system; research and development; sensor; simulation; size; soft tissue; software development; sound; tool; usability; virtual

DESCRIPTION (provided by applicant): We propose to develop a toolbox for estimation, simulation and control of multi-joint movements. Our immediate goal is to facilitate research in Motor Control, by providing access to advanced computational methods and making such methods an integral part of the hypothesis generation-and-testing cycle. The estimation component of the toolbox will enable researchers to accurately compute multi-joint movement trajectories as well as limb sizes from motion capture data, without spending hours to place markers at precise locations and redesign setups to ensure that every marker is always visible. The control component will make it possible to formulate mathematically-sound hypotheses about the control strategies used by the brain, and automatically synthesize detailed control laws corresponding to the user's hypotheses. These control laws will then be applied to realistic musculo-skeletal models, using the simulation component, and the predicted behavior will be compared to experimental data in terms of kinematics, contact forces and EMGs. In case of a mismatch the toolbox will be able to netune any free parameters of the controller, and also search the library of candidate control strategies and identify the one which best agrees with the data. Our longer-term goal is to assist clinicians and engineers designing new treatments such as reconstructive surgery and functional electric stimulation. Testing candidate control mechanisms on simulated musculo-skeletal dynamics can greatly reduce the undesirable trial-and-error iterations. Customizations necessary for clinical use are left outside the scope of this project, however they will be possible once the core functionality is developed in a system with open design. The toolbox will be written in Matlab, with some C++ components, and will be freely available for academic, research and non-prot purposes. Project narrative We propose to develop a toolbox for estimation, simulation and control of multi-joint movement. Our immediate goal is to facilitate research in the eld of Motor Control by providing access to advanced computational methods presently beyond the reach of many investigators. While tools for simulating musculo-skeletal dynamics already exist, simulation alone is rarely suffcient to advance our understanding of motor function. Here we will combine simulation with automatic controllers capable of driving realistic musculo-skeletal models, and provide tools for estimating multi-joint movements from motion capture data. Our longer-term goal is to assist clinicians and engineers designing new treatments such as reconstructive surgery and functional electric stimulation.

描述(申请人提供)：我们建议开发一个工具箱，用于估计、模拟和控制多关节运动。我们的直接目标是通过提供对先进计算方法的访问，并使这些方法成为假设生成和测试周期中不可或缺的一部分，来促进电机控制的研究。该工具箱的估计组件将使研究人员能够根据运动捕捉数据准确计算多关节运动轨迹以及肢体大小，而无需花费数小时在精确位置放置标记并重新设计设置，以确保每个标记始终可见。控制部件将使其有可能就大脑使用的控制策略制定数学上合理的假设，并自动合成与用户假设相对应的详细控制规律。然后，这些控制律将使用仿真组件应用于真实的肌肉骨骼模型，并将预测的行为与实验数据在运动学、接触力和肌电方面进行比较。在不匹配的情况下，工具箱将能够联网控制器的任何自由参数，还可以搜索候选控制策略库并识别最符合数据的控制策略库。我们的长期目标是帮助临床医生和工程师设计新的治疗方法，如重建手术和功能性电刺激。在模拟肌肉骨骼动力学上测试候选控制机制可以极大地减少不必要的试错迭代。临床使用所需的定制不在本项目的范围之内，但一旦在开放式设计的系统中开发了核心功能，这些定制就会成为可能。该工具箱将用MatLab编写，带有一些C++组件，可以免费用于学术、研究和非PROT目的。项目简介我们建议开发一个工具箱，用于估计、模拟和控制多关节运动。我们的直接目标是通过提供目前许多研究人员无法获得的先进计算方法来促进运动控制领域的研究。虽然模拟肌肉骨骼动力学的工具已经存在，但仅靠模拟很少足以促进我们对运动功能的理解。在这里，我们将结合模拟和自动控制器，能够驱动逼真的肌肉骨骼模型，并提供从运动捕捉数据估计多关节运动的工具。我们的长期目标是帮助临床医生和工程师设计新的治疗方法，如重建手术和功能性电刺激。