NRI: Small: Modeling, Quantification, and Optimization of Prosthesis-User Interface

NRI：小型：假体用户界面的建模、量化和优化

• 批准号: 1317379
• 负责人: Levi Hargrove
• 金额: $ 99.99万
• 依托单位: Rehabilitation Institute of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1317379&HistoricalAwards=false
• 关键词: NRI; Small; Modeling; Quantification; Optimization

PI: Sensinger, J. W.; Hargrove, L.; and Kording, K. P.Proposal Number: 1317379Problem Description: Better robotic prostheses can dramatically improve the quality of life for the more than 40,000 Americans with an upper limb amputation, many of whom reject existing devices because they have trouble controlling them in the same intuitive, subconscious way that they controlled their intact arms. Prosthesis control is difficult because amputees experience great uncertainty both with respect to whether their device will respond appropriately to their control signals and whether sensory feedback cues accurately reflect the actual movement. Researchers have focused on improving isolated aspects of control, for example by improving filters or mimicking able-bodied sensory cues through haptic devices, but these approaches have minimally reduced the uncertainty of prosthesis control. Human interaction with a prosthesis is a multifaceted, time-varying problem that is difficult to solve. What is missing from robotic prosthesis research are principled methods for optimizing control strategies and sensory cues which take into account behavioral choices people are known to make in the face of high uncertainty.Intellectual Merit: The proposed research is innovative because it poses the co-robot problem in a broader context that incorporates the highly sophisticated behavioral decisions that humans make in optimizing their control strategy and sensory cues. This principled approach is able to integrate multiple effects in ways that were not possible using previous approaches. For example, the proposed approach naturally incorporates the fact that people prefer to use less exerted effort to accomplish a task, but tolerate more effort during portions of movement that require greater precision (e.g. final portion of a trajectory). On the other hand, the approach does not favor high-certainty haptic cues if those cues provide redundant information to existing sensory cues such as vision, or if the haptic information does not reduce the uncertainty of controllable system dynamics. Due to the large sources of control-signal noise present in amputees, the proposed work will lead to improved techniques within the fields of computational motor control and optimal control. This research builds on the team?s extensive experience in the design and control of upper-limb prostheses and in developing the field of computational motor control. Achievement of the proposed aims will contribute to the field of robotic control and to such diverse fields as human-robot interaction, perception, manipulation, and exoskeletons.Broader Impacts: True biomimetic prostheses, exoskeletons, and humanoid robot control will not be possible until there is a firm understanding of how humans integrate with these co-robots in the face of interacting sources of uncertainty. This computational motor project will provide transformative insight into how humans control movement in the presence of large uncertainty and thus fill a critical gap in the knowledge base of this field. The framework developed in this research will be of great interest to the motor-control research community and may be useful in the restoration of other movement disorders such as spinal cord injury and stroke. The lead institution of this proposal, the Rehabilitation Institute of Chicago (RIC), is consistently ranked the top rehabilitation hospital in the country. The close proximity of research and clinical excellence within RIC ensures that the benefits resulting from this work will be quickly disseminated to prosthesis users. The research team will also seek to reach a broader audience?the laboratories at the RIC are regularly visited by students from local high schools and universities, and the RIC also contributes to outreach activities within inner-city Chicago. These outreach programs promote an awareness of rehabilitation research and an enthusiasm for pursuing a career in engineering. Additionally, the team will develop a K-12 educational module based on the template of the successful Summer School in Computational Sensory-Motor Neuroscience developed at Northwestern University and Queen?s University, which will provide a combination of theory and student-driven experimentation using games that will address many of the Illinois Learning Standards in science, math, and English language arts.

PI：Sensinger，J.W.; Hargrove，L.; Kording，K. P.提案编号：1317379问题描述：更好的机器人假肢可以显著改善40，000多名上肢截肢的美国人的生活质量，其中许多人拒绝使用现有设备，因为他们难以像控制完好手臂那样直观、潜意识地控制这些设备。假肢控制是困难的，因为截肢者在他们的设备是否会适当地响应他们的控制信号以及感觉反馈提示是否准确地反映实际运动方面都经历了很大的不确定性。研究人员专注于改善控制的孤立方面，例如通过改进过滤器或通过触觉设备模仿健全的感官提示，但这些方法最大限度地减少了假肢控制的不确定性。人类与假肢的互动是一个多方面的、随时间变化的问题，很难解决。机器人假肢研究缺少的是优化控制策略和感官线索的原则性方法，这些方法考虑到人们在面对高度不确定性时所做的行为选择。智力优势：这项研究是创新的，因为它提出了共同的-机器人问题在更广泛的背景下，结合了高度复杂的行为决策，人类在优化他们的控制策略和感官线索。这种原则性的方法能够以使用以前的方法不可能的方式整合多种效果。例如，所提出的方法自然地结合了这样的事实，即人们更喜欢使用较少的努力来完成任务，但是在需要更高精度的移动部分（例如，轨迹的最后部分）期间容忍更多的努力。另一方面，该方法不赞成高确定性的触觉线索，如果这些线索提供冗余的信息，现有的感官线索，如视觉，或者如果触觉信息不减少可控系统动态的不确定性。由于截肢者体内存在大量控制信号噪音来源，拟议的工作将导致计算运动控制和最优控制领域的技术改进。这项研究建立在团队的基础上？在上肢假肢的设计和控制以及计算运动控制领域的发展方面拥有丰富的经验。这些目标的实现将有助于机器人控制领域以及人机交互、感知、操纵和外骨骼等不同领域的发展。更广泛的影响：真正的仿生假肢、外骨骼和仿人机器人控制将不可能实现，除非我们能够确切地理解人类如何与这些合作机器人在面对不确定性的交互源时进行整合。这个计算运动项目将为人类如何在存在很大不确定性的情况下控制运动提供变革性的见解，从而填补该领域知识基础的关键空白。本研究中开发的框架将引起运动控制研究界的极大兴趣，并可能有助于恢复其他运动障碍，如脊髓损伤和中风。该提案的牵头机构，芝加哥康复研究所（RIC），一直被评为全国顶级康复医院。RIC内部研究和临床卓越的紧密联系确保了这项工作所带来的好处将迅速传播给假肢用户。研究团队还将寻求接触更广泛的受众？来自当地高中和大学的学生定期访问RIC的实验室，RIC还为芝加哥市中心的外展活动做出贡献。这些外展计划促进了康复研究的意识和追求工程事业的热情。此外，该团队将开发一个K-12教育模块的基础上，成功的暑期学校在计算感觉运动神经科学在西北大学和皇后？该大学将提供理论和学生驱动的实验相结合的游戏，将解决许多伊利诺伊州的学习标准，在科学，数学和英语语言艺术。