Exploration of optimal prosthesis feedback information using computational motor control

使用计算运动控制探索最佳假肢反馈信息

• 批准号: RGPIN-2014-06464
• 负责人: Sensinger, Jonathon
• 金额: $ 2.26万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708322
• 关键词: Exploration; optimal; prosthesis; feedback; information

Better robotic prostheses can dramatically improve the quality of life for persons 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 that take into account behavioral choices people are known to make in the face of high uncertainty. Our unique approach is to use an optimization strategy that incorporates the behavioral decisions that humans intuitively make in order to deal with uncertainty. For healthy subjects, computational motor control models based on human behavioral data describe very well how subjects learn, estimate, and control. This is even true for cases that are analogous to prosthesis use, such as mapping non-intuitive joints to abstract degrees of freedom or signal-dependent noise. This suggests that those models could also predict how an amputee learns to control a prosthesis using their noisier control signals and limited sensory feedback. Building models and calibrating them with experiments to make them predictive will allow us to study how different decoder and feedback designs would affect behavior. This model-driven approach promises faster and more efficient prosthesis design. We plan to quantify the uncertainty that amputees attribute to various sources (their control signals, the prosthesis, and the world); to develop novel controllers that reduce the uncertainty of control; and to provide haptic sensory cues that work synergistically with available sensors and control strategies to reduce uncertainty. The proposed research is innovative because it poses the control problem in a broader context that incorporates the highly sophisticated behavioral decisions that humans make in optimizing their control strategy and sensory cues. This approach is able to integrate multiple effects in ways that were not possible using previous approaches. For example, our 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, our 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, our work will lead to improved techniques within the fields of computational motor control and optimal control. This research builds on our team's extensive experience in the design and control of upper-limb prostheses and our collaborator's experience in 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, and will provide a rich platform for education at all levels.

更好的机器人假肢可以极大地改善上肢截肢患者的生活质量，他们中的许多人拒绝使用现有的设备，因为他们无法像控制完整手臂那样直观、潜意识地控制它们。假肢控制很困难，因为截肢者在他们的设备是否会对他们的控制信号做出适当响应以及感觉反馈线索是否准确反映实际运动方面都经历很大的不确定性。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觉线索，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方法，这些方法考虑了人们在面对高度不确定性时所做出的已知行为选择。 我们独特的方法是使用优化策略，该策略结合了人类直观地做出的行为决策，以应对不确定性。对于健康受试者，基于人类行为数据的计算运动控制模型很好地描述了受试者如何学习、估计和控制。对于类似于假肢使用的情况也是如此，例如将非直观关节映射到抽象自由度或信号相关噪声。这表明这些模型还可以预测截肢者如何利用嘈杂的控制信号和有限的感觉反馈来学习控制假肢。构建模型并通过实验校准它们以使其具有预测性，这将使我们能够研究不同的解码器和反馈设计将如何影响行为。这种模型驱动的方法有望实现更快、更高效的假肢设计。我们计划量化截肢者归因于各种来源（他们的控制信号、假肢和世界）的不确定性；开发新型控制器以减少控制的不确定性；并提供与可用传感器和控制策略协同工作的触觉感官线索，以减少不确定性。 拟议的研究具有创新性，因为它在更广泛的背景下提出了控制问题，其中包含了人类在优化控制策略和感官线索时做出的高度复杂的行为决策。这种方法能够以以前的方法无法实现的方式整合多种效果。例如，我们的方法自然地结合了这样一个事实，即人们更喜欢使用较少的努力来完成任务，但在需要更高精度的运动部分（例如轨迹的最后部分）可以容忍更多的努力。另一方面，如果高确定性触觉提示为现有的感官提示（例如视觉）提供冗余信息，或者触觉信息不会降低可控系统动力学的不确定性，那么我们的方法不支持高确定性触觉提示。由于截肢者存在大量控制信号噪声源，我们的工作将改进计算运动控制和最优控制领域的技术。这项研究建立在我们团队在上肢假肢设计和控制方面的丰富经验以及我们的合作者在计算运动控制领域的经验的基础上。所提出目标的实现将为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做出贡献，并将为各级教育提供丰富的平台。