Prosthesis Control by Forward Dynamic Simulation of the Intact Biomedical system

通过完整生物医学系统的正向动态仿真进行假肢控制

基本信息

批准号：
8252162
负责人：
Wendy M Murray
金额：
$ 43.62万
依托单位：
REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-04-05 至 2015-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8252162
关键词：
Address Algorithms Amputation Amputees Biomechanics Clinical Complex Data Development Devices Ensure Evaluation Fingers Forearm Freedom Goals Hand Hand functions Human Individual Joints Learning Limb structure Link Manuals Measures Mechanics Modeling Motion Motor Movement Muscle Output Performance Persons Physiological Population Posture Prosthesis Rehabilitation therapy Residual state Running Sign Language Signal Transduction Simulate Spinal cord injury Stroke System Techniques Thumb structure Time Work Wrist base design expectation kinematics meetings motor control public health relevance simulation theories

Address Algorithms Amputation Amputees Biomechanics Clinical Complex Data Development Devices Ensure Evaluation Fingers Forearm Freedom Goals Hand Hand functions Human Individual Joints Learning Limb structure Link Manuals Measures Mechanics Modeling Motion Motor Movement Muscle Output Performance Persons Physiological Population Posture Prosthesis Rehabilitation therapy Residual state Running Sign Language Signal Transduction Simulate Spinal cord injury Stroke System Techniques Thumb structure Time Work Wrist base design expectation kinematics meetings motor control public health relevance simulation theories

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项目摘要

DESCRIPTION (provided by applicant): Persons with recent hand amputations expect modern hand prostheses to function like intact hands. Current state-of-the-art electric prosthetic hands are generally single degree-of-freedom (opening and closing) devices that are controlled using only two muscle signals. As a result, most state-of-the-art devices fail to meet user's expectations and are under-utilized or rejected. Because of this, advances in mechanical hardware are directed toward providing functionality comparable to the intact human hand. Despite such advances, the performance of sophisticated hand prostheses remains limited by the ability to control them via physiological (e.g., electromyographic) signals sensed from the user. In general, prosthetic devices that support multiple degree-of-freedom movements for any limb require sequential control, implementing locking mechanisms or special switch signals to change from one degree-of-freedom to another. There is a large, unmet need for control algorithms that allow simultaneous control of multiple degrees-of-freedom and are not difficult for the user to learn. In this study, we will implement a biomechanical modeling approach to develop a control algorithm that predicts the hand and wrist motions that would occur in an intact hand given the electromyographic (EMG) signals measured from the residual muscles of an amputee's forearm. The objectives for this proposal are to first characterize the function of the hand muscles in creating complex hand motions in the intact hand and to then develop the controller. To accomplish these objectives, extrinsic muscle activity and joint kinematics will be quantified as individuals produce a subset of postures from the manual alphabet of American Sign Language (ASL), and perform two prehensile tasks. Recorded muscle activity will define the control signals available from the extrinsic muscles during complex motions, and will become input for biomechanical simulations, which will be used to identify how effectively postures can be achieved without the contributions from the intrinsic muscles of the hand (the subset of muscles lost to amputation). Results will direct the mechanical design of prosthetic hands to effectively compensate for the mechanical actions of the missing intrinsic muscles. Ultimately, a prosthetic hand is intended to be used to manipulate objects. Thus, we will implement recent developments in variational integration theory to develop real-time simulations that incorporate endpoint forces, such as those found when the fingertips are in contact with an object, and other constraints required to simulate the hand interacting with external objects. Upon completion of the simulation work, a controller that drives the artificial hand based on user-generated muscle signals will be developed and implemented. Accomplishing the goals of this project will address a critical barrier to clinical implementation and user acceptance of multi-function prosthetic hands. PUBLIC HEALTH RELEVANCE: At its core, this project aims to deliver a real-time simulator of complex, multi-degree of freedom human hand motions, and link it to the hardware necessary to control state-of-the-art multi-function artificial hands. Such a system will enable the evaluation of many different approaches to the control of hand prostheses, facilitate the study of motor control of hand movement, and will have applications to rehabilitation of hand function in many populations, such as spinal cord injury and stroke.

描述（由申请人提供）：具有近期手截肢的人期望现代的手提假肢像完整的手一样发挥作用。当前最新的电肢体假手术通常是仅使用两个肌肉信号控制的单一自由度（开放和关闭）设备。结果，大多数最先进的设备无法满足用户的期望，并且未能充分利用或拒绝。因此，机械硬件的进步旨在提供与完整人手相当的功能。尽管有这样的进步，但复杂的手提假肢的性能仍然受到用户感受到的生理（例如肌电图）信号来控制它们的能力。通常，支持多个肢体的多个自由度运动的假肢设备需要顺序控制，实现锁定机制或特殊的开关信号，以从一个自由度变为另一个。对控制算法的需求很大，可以同时控制多个自由度，并且用户不难学习。在这项研究中，我们将实施一种生物力学建模方法来开发一种控制算法，该算法可以预测鉴于截肢者前臂的残留肌肉测量的肌电图（EMG）信号，该算法将在完整的手中发生。该提案的目标是首先表征手肌肉在完整手中创建复杂的手动运动，然后开发控制器的功能。为了实现这些目标，将量化外在的肌肉活动和关节运动学，因为个体从美国手语（ASL）手动字母（ASL）中产生了一部分姿势，并执行了两项术前任务。记录的肌肉活性将定义复杂运动过程中外肌外肌的控制信号，并将成为生物力学模拟的输入，该模拟将用于确定如何在没有手的内在肌肉（肌肉丢失的肌肉子集中降低到截肢的肌肉）的情况下可以有效地实现姿势）。结果将指示假肢的机械设计有效地补偿缺失的内在肌肉的机械作用。最终，假肢旨在用来操纵物体。因此，我们将实施各种整合理论的最新发展，以开发结合端点力的实时模拟，例如当指尖与对象接触时发现的力量以及模拟手动与外部对象相互作用所需的其他约束。模拟工作完成后，将开发和实施基于用户生成的肌肉信号的人造手的控制器。实现该项目的目标将解决临床实施和用户接受多功能假肢的关键障碍。公共卫生相关性：本项目的核心旨在提供复杂，多度人类手动运动的实时模拟器，并将其链接到控制最先进的多功能人工手的必要硬件。这样的系统将能够评估许多不同的方法来控制手部假肢，促进手动移动的运动控制，并在许多人群中（例如脊髓损伤和中风）中应用手动恢复。