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

描述（由申请人提供）：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。我们的直接目标是促进电机控制的研究，通过提供先进的计算方法，并使这些方法成为假设生成和测试周期的组成部分。工具箱的估算组件将使研究人员能够根据动作捕捉数据准确计算多关节运动轨迹和肢体尺寸，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。控制组件将有可能对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，这些控制规律将应用于现实的肌肉骨骼模型，使用仿真组件，并将预测的行为与运动学，接触力和肌电信号方面的实验数据进行比较。在不匹配的情况下，工具箱将能够调整控制器的任何自由参数，也可以搜索候选控制策略库并识别最符合数据的控制策略。我们的长期目标是协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大大减少不必要的试错迭代。临床使用所需的定制不属于本项目的范围，但是一旦在具有开放设计的系统中开发出核心功能，它们将成为可能。该工具箱将用Matlab编写，并带有一些c++组件，并且将免费提供给学术、研究和非专业用途。我们建议开发一个工具箱，用于多关节运动的估计、模拟和控制。我们的直接目标是通过提供目前超出许多研究者范围的先进计算方法来促进电机控制领域的研究。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，我们将结合仿真与自动控制器能够驱动逼真的肌肉骨骼模型，并提供工具估计多关节运动从动作捕捉数据。我们的长期目标是协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。

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.