CHS: Medium: Collaborative Research: Novel Optimal Control for Co-Adaptation of Human and Powered Lower Limb Prosthesis

CHS：媒介：协作研究：人类和动力下肢假肢共同适应的新型最优控制

• 批准号: 1563921
• 负责人: Jennie Si
• 金额: $ 45.78万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563921&HistoricalAwards=false
• 关键词: CHS; Medium; Collaborative; Research; Novel

Emerging powered lower limb prostheses hold great promise for restoring normative locomotion in amputees. However, these robotic devices currently lack inter- and intra-wearer adaptability to cope with wearers' physical variations and changes. Frequent manual and heuristic adjustment in clinics is required, which limits the practical use of these advanced prostheses. A new generation of prosthesis control that is intelligent, adaptable, and interactive is needed to better support walking function and improve the quality of life of lower limb amputees. The PIs' long-term research goal is to create bionic legs that can adapt to the individual amputee's physical and cognitive capabilities, coordinate with the wearer's movement and intent, adjust to changing environments, and essentially restore the full function of patients with lower limb impairments. To this end, the PIs' objective in this project is to create a novel optimal control framework for these prostheses. They will systematically address the challenge of supporting automatic adaptation to the wearer's physical capability while achieving desired gait performance for the integrated amputee-prosthesis system. And they will provide a preliminary design and evaluation for an interactive interface that would allow wearers to personalize prosthesis control safely and easily. Project outcomes will open up a new frontier of wearable robotics and lay the foundation for clinical translations of these innovative devices, which will impact not only the prosthetics and orthotics industry but also the robotics community by providing new knowledge relating to human-robot interaction, the biomechanics and neuromotor control community by elucidating the control mechanism of amputee locomotion, and healthcare in general by providing innovative and cost-effective prosthesis solutions.The new amputee-prosthesis performance-based framework for control of powered lower limb prostheses which this work will introduce represents a departure from existing approaches that mainly focus on design for the prosthesis (a local machine), in that it adopts a global approach by accounting for co-adaptation between amputees and prostheses in order to provide optimal, personalized assistance based on wearers' physical conditions. The PIs will use approximate dynamic programming (ADP) to achieve the global control goal. Such innovative use of ADP will provide an opportunity to demonstrate its optimal adaptive control capability in a new test domain of co-adaptive robotic prosthesis, a unique and significant challenge only seen in human wearable robotics but not in lifeless robots. The ADP scheme is based on approximation and learning that alleviate problems associated with the requirement of accurately modeling wearers' neuromuscular control and dynamics that is difficult, if not impossible, to achieve. Additionally, the PIs will conduct an experimental investigation on subjects with transfemoral amputations of the interactions between amputees and prostheses, including evaluations of the compensatory strategies of amputees, and discrepancy assessment between subjective (human) and objective (machine) preferences in prosthesis control.

新兴的动力下肢假肢有很大的希望恢复截肢者的规范运动。 然而，这些机器人设备目前缺乏穿戴者间和穿戴者内的适应性以科普穿戴者的身体变化和改变。 在临床上需要频繁的手动和启发式调整，这限制了这些先进假体的实际应用。 需要智能、适应性和交互式的新一代假肢控制，以更好地支持行走功能并提高下肢截肢者的生活质量。 PI的长期研究目标是创造仿生腿，可以适应截肢者的身体和认知能力，与佩戴者的运动和意图相协调，适应不断变化的环境，并从根本上恢复下肢损伤患者的全部功能。 为此，PI在该项目中的目标是为这些假体创建一个新的最佳控制框架。 他们将系统地解决支持自动适应佩戴者的身体能力的挑战，同时实现所需的步态性能的综合截肢假肢系统。 他们将为交互式界面提供初步设计和评估，使佩戴者能够安全轻松地进行个性化假肢控制。 项目成果将开辟可穿戴机器人技术的新前沿，并为这些创新设备的临床转化奠定基础，这不仅将影响假肢和矫形行业，还将通过提供与人机交互有关的新知识，生物力学和神经运动控制社区阐明截肢者运动的控制机制，通过提供创新的和具有成本效益的假肢解决方案，在整个医疗保健领域发挥着重要作用。本文将介绍一种新的基于截肢者假肢性能的动力下肢假肢控制框架，该框架与现有的主要侧重于假肢设计的方法不同（本地机器），因为它通过考虑截肢者和假肢之间的共同适应采用全局方法，以便根据佩戴者的身体状况提供最佳的个性化帮助。 PI将使用近似动态规划（ADP）来实现全局控制目标。 ADP的这种创新性使用将提供一个机会，在一个新的测试领域的共适应机器人假肢，一个独特的和重大的挑战，只有在人类可穿戴机器人，但不是在无生命的机器人，展示其最佳的自适应控制能力。 ADP方案基于近似和学习，其缓解了与精确地建模穿戴者的神经肌肉控制和动力学的要求相关联的问题，该要求难以实现（如果不是不可能的话）。 此外，PI将对经股动脉截肢的受试者进行截肢者与假体之间相互作用的实验研究，包括评估截肢者的补偿策略，以及评估假体控制中主观（人）和客观（机器）偏好之间的差异。

项目成果

Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis

• DOI: 10.1109/tcyb.2019.2890974
• 发表时间: 2020-06-01
• 期刊: IEEE TRANSACTIONS ON CYBERNETICS
• 影响因子: 11.8
• 作者: Wen, Yue;Si, Jennie;Huang, He (Helen)
• 通讯作者: Huang, He (Helen)

Wearer-Prosthesis Interaction for Symmetrical Gait: A Study Enabled by Reinforcement Learning Prosthesis Control

• DOI: 10.1109/tnsre.2020.2979033
• 发表时间: 2020-04-01
• 期刊: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
• 影响因子: 4.9
• 作者: Wen, Yue;Li, Minhan;Huang, He
• 通讯作者: Huang, He