EAGER: Principles of Motor Skills (re)Learning When Using Powered Exoskeletons

EAGER：使用动力外骨骼时（重新）学习运动技能的原则

• 批准号: 2207515
• 负责人: Divya Srinivasan
• 金额: $ 29.99万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207515&HistoricalAwards=false
• 关键词: EAGER; Principles; Motor; Skills; re

Exoskeletons have great potential to augment physical power while preserving human skill in dynamic industrial environments. However, many industrial tasks involving tools are complex, and require high dexterous control. Workers also have different levels of experience and possess different physical and cognitive skill-sets. Hence, exoskeleton acceptability and appropriate training are key factors that will determine future industrial applications. This EArly-concept Grant for Exploratory Research (EAGER) project will promote the progress of science and advance the national health, prosperity and welfare by identifying basic principles of human motor skill learning when using multi-joint arm exoskeletons to perform complex tasks. By introducing exoskeletons to users with varying levels of task-relevant skills at baseline, and by modeling learning using a comprehensive set of neuromotor measures, the project promises to discover fundamental principles that will guide the design of adaptable exoskeleton control algorithms and personalized training protocols that can produce true collaboration between exoskeletons and individuals with diverse skills. Broader impacts of the work include workshops focused on increasing workers awareness of exoskeletons and their potential applications in the workplace, as well as opportunities for worker-skills training using exoskeletons.This work will advance knowledge of the complex learning dynamics that arise in human-exoskeleton systems through systematic manipulation of critical mind-motor-machine factors influencing the collaboration between man and machine. Our novel contribution will be to expand the generalizability of a two-stage multi-rate model of motor adaptation by studying short- and long-term adaptations with exoskeleton use by a broad range of users with varying skill and tasks of varying complexity. The concept of persistent excitation will be leveraged to perturb the dynamical conditions under which the human accomplishes the task, to maximize human learning from the machine, while an extremum seeking control approach will be implemented for the machine to learn from the human response. The two concepts will be combined in a novel exoskeleton control algorithm for adaptively tuning exoskeleton parameters (such as torque amplification) as the human’s use of the exoskeleton evolves.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

外骨骼在增强体力的同时，在动态工业环境中保留人类技能方面具有巨大的潜力。然而，许多涉及工具的工业任务是复杂的，并且需要高度灵巧的控制。工人也有不同的经验水平，并拥有不同的身体和认知技能。因此，外骨骼的可接受性和适当的训练是决定未来工业应用的关键因素。EARLY概念探索性研究资助（EAGER）项目将通过确定使用多关节手臂外骨骼执行复杂任务时人类运动技能学习的基本原则，促进科学进步，促进国家健康，繁荣和福利。通过将外骨骼引入到具有不同水平的任务相关技能的用户中，并通过使用一套全面的神经运动测量来建模学习，该项目有望发现基本原则，这些原则将指导自适应外骨骼控制算法和个性化训练协议的设计，这些算法和协议可以在外骨骼和具有不同技能的个人之间产生真正的协作。 这项工作的更广泛影响包括研讨会，重点是提高工人对外骨骼及其在工作场所的潜在应用的认识，以及使用外骨骼进行工人技能培训的机会。这项工作将通过系统地操纵影响人机协作的关键思维-运动-机器因素，推进对人类-外骨骼系统中出现的复杂学习动力学的认识。我们的新的贡献将是扩大的两阶段多速率模型的运动适应的普遍性，通过研究短期和长期的适应与外骨骼使用的广泛的用户具有不同的技能和不同的复杂性的任务。持续激励的概念将被用来扰动人类完成任务的动态条件，以最大限度地提高人类从机器中的学习，而极值搜索控制方法将被实现，以使机器从人类的响应中学习。这两个概念将结合在一种新的外骨骼控制算法中，用于随着人类对外骨骼的使用而自适应地调整外骨骼参数（如扭矩放大）。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。