CAREER: A Task-Invariant Customization Framework for Lower-Limb Exoskeletons to Assist Volitional Human Motion

职业生涯：用于辅助人类意志运动的下肢外骨骼的任务不变定制框架

• 批准号: 2340261
• 负责人: Ge Lv
• 金额: $ 57.09万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2029-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340261&HistoricalAwards=false
• 关键词: CAREER; Task; Invariant; Customization; Framework

This Faculty Early Career Development (CAREER) award will support research that advances knowledge in the control and optimization of lower-limb exoskeletons in providing their human users with customized assistance across locomotor tasks. Conventional customization paradigms often aim at optimizing parameters of pre-defined torque profiles for specific locomotor tasks, which cannot accommodate the continuously varying activities humans perform every day. Prevalent approaches also place emphasis on minimizing slow-converging energy expenditures associated with human locomotion, which is important for able-bodied persons but might not be of high priority for individuals with pathological gaits. The goal of this project is to provide a paradigm shift from task-specific, slow convergent customization to task-invariant, rapid customization. This research will facilitate active learning and adaptation of lower-limb exoskeletons to drastically reduce the cost of gait rehabilitation for nearly a million Americans who sustain a new stroke every year. Key parameters throughout gait rehabilitation such as body-weight support ratio will be automatically customized based on stroke subject's training progress, which will otherwise be tuned by a team of therapists and engineers that usually lasts for hours. This research will also promote the use of exoskeletons for able-bodied users in manufacturing sites, warehouses, battlefields, and other relevant scenes by reducing the associated costs in control parameter customization. The integrated education plan will help cultivate the next-generation wearable robot researchers and motivating K-12 students to further pursue STEM degrees. The human subject studies and exhibits will promote the awareness of wearable technologies among the public, especially among traditionally underrepresented groups.The goals of this project are to: 1) investigate invariant assistive strategies in continuously varying locomotor tasks and environments, 2) construct a complete framework for customizing exoskeleton assistance across locomotor tasks, and 3) understand how the customization framework facilitates assistance adaptations to different user’s volitional motion and activities. Utilizing a two-layer optimization structure will rapidly determine task-invariant assistive strategies in the inner-loop through tracking the desired energetics or centroidal momentum of a virtual reference model, meanwhile updating its parameters in the outer-loop based on human performance-based cost functions. The research the PI and his team will conduct will investigate task-invariant assistive strategies that alter human body energetics and centroidal momentum, construct a complete, human-in-the-loop customization framework that rapidly customizes these two quantities, and validate its efficacy via experiments on human subjects across various locomotor tasks.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.

该学院早期职业发展（Career）奖将支持在下肢外骨骼控制和优化方面的研究，为人类用户提供定制的运动任务帮助。传统的定制范例通常旨在优化特定运动任务的预定义扭矩曲线参数，这无法适应人类每天进行的不断变化的活动。流行的方法还强调最小化与人体运动相关的慢收敛能量消耗，这对健全的人很重要，但对患有病理性步态的人可能不是很重要。该项目的目标是提供从特定于任务的、缓慢的收敛定制到任务不变的、快速定制的范式转换。这项研究将促进下肢外骨骼的主动学习和适应，大大降低步态康复的成本，为每年近100万美国人维持新的中风。步态康复过程中的关键参数，如体重支撑比，将根据中风患者的训练进度自动定制，否则将由治疗师和工程师团队进行调整，通常持续数小时。本研究还将通过降低控制参数定制的相关成本，促进外骨骼在制造现场、仓库、战场和其他相关场景中为健全用户使用。综合教育计划将有助于培养下一代可穿戴机器人研究人员，并激励K-12学生进一步攻读STEM学位。人体主题研究和展览将促进公众对可穿戴技术的认识，特别是在传统上代表性不足的群体中。本项目的目标是：1)在不断变化的运动任务和环境中研究不变的辅助策略；2)构建一个完整的框架来定制跨运动任务的外骨骼辅助；3)了解定制框架如何促进辅助适应不同用户的意志运动和活动。利用双层优化结构，通过跟踪虚拟参考模型的期望能量或质心动量，在内环快速确定任务不变的辅助策略，同时在外环根据基于人类性能的成本函数更新其参数。PI和他的团队将进行的研究将调查任务不变的辅助策略，改变人体能量和质心动量，构建一个完整的人在循环定制框架，快速定制这两个量，并通过各种运动任务的人类受试者实验验证其功效。该奖项反映了美国国家科学基金会的法定使命，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。