NRI: INT: Wearable Robots for the Community: Personalized Assistance using Human-in-the-loop Optimization

NRI：INT：社区可穿戴机器人：使用人机交互优化的个性化协助

• 批准号: 1925085
• 负责人: Conor Walsh
• 金额: $ 149.97万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925085&HistoricalAwards=false
• 关键词: NRI; INT; Wearable; Robots; Community

This project will advance the progress of customizable soft robotic exosuits for seamless assistance in everyday activities, to mitigate impairment or to augment normal functionality. Soft robotic exosuits are a new class of functional clothing that apply mechanical assistance to wearers' joints in parallel with their muscles. Millions of Americans with neurologically-based walking impairments, such as the nearly seven million people living post-stroke, could benefit from the unobtrusive assistance that exosuits can provide during walking. Similarly, healthy individuals who carry heavy loads over long distances (e.g., first responders or soldiers) could benefit from using exosuit technology to partially alleviate their burden. Synchronizing mechanical assistance to the wearer's natural rhythm is essential to realizing the potential of these devices. The soft robotic exosuits created in this project will monitor the wearer's walking pattern using body-worn sensors, apply machine learning methods to personalize the robotic assistance pattern, and continuously update that assistance pattern as the wearer's gait changes. In contrast, current methods rely on expert clinicians and technicians to manually tune assistance patterns. This project will contribute new knowledge to advance the national health and prosperity. The multidisciplinary research team includes roboticists, movement scientists, and clinicians, who will work closely with persons poststroke in laboratory and clinical environments.Recent work developing human-in-the loop optimization strategies for exoskeletons suggests that it is possible to lower the metabolic cost of walking by treating the exosuit control as an optimization problem, with direct measurements of metabolic cost serving as the objective. However, recording such measurements require bulky devices that interfere with normal breathing, meaning the method cannot currently work in everyday environments. Additionally, lowering the metabolic cost of walking is not the only goal of exosuits in persons poststroke, whose gait is characterized by slow movement and compensations such as hip hiking, circumduction, and vaulting. Successful human-in-the-loop optimization in clinical populations will need to address all these factors, not just metabolic cost. An initial basic-science exploration will find effective proxies for metabolic cost in healthy populations and develop a multi-objective function in people poststroke. In both cases, the objective function must be unobtrusive to measure, accurate, and responsive to changes in exosuit control. A second implementation phase will use human-in-the-loop optimization to automatically adapt control parameters of portable systems assisting healthy individuals and people poststroke.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.

该项目将推动可自定义的软机器人外套件的进度，以在日常活动中无缝帮助，减轻损害或增加正常功能。柔软的机器人外套件是一类新型的功能性服装，它们与肌肉并联为佩戴者的关节应用机械援助。数百万的美国人患有基于神经系统的步行障碍，例如中风后居住的近700万人，可以从外来西服在步行过程中可以提供的毫不动心的援助受益。同样，在长距离（例如急救人员或士兵）上承受重重负荷的健康人可能会从使用外套技术部分减轻其负担中受益。将机械辅助与佩戴者的自然节奏同步对于意识到这些设备的潜力至关重要。该项目中创建的柔软机器人外观将使用戴着身体的传感器监视佩戴者的行走图案，应用机器学习方法来个性化机器人援助模式，并随着佩戴者的步态变化而不断更新该辅助模式。相比之下，当前的方法依靠专家临床医生和技术人员手动调整援助模式。该项目将有助于新知识，以促进国家健康和繁荣。多学科研究团队包括机器人，运动科学家和临床医生，他们将与实验室和临床环境中的中风后的人紧密合作。开发外骨骨骼的人体循环优化策略表明，可以通过将Exosiuit控制作为优化的成本来降低代谢成本，以降低行走的代谢成本，并将其作为目标成本，并将其作为METBERICOID，该目标量为目标。但是，记录此类测量需要庞大的设备来干扰正常的呼吸，这意味着该方法目前无法在日常环境中起作用。此外，降低步行的代谢成本并不是中风后人外钉的唯一目标，后者的步态的特征是运动缓慢和诸如髋关节远足，派遣和拱顶等补偿。在临床人群中成功的人类在环境优化中将需要解决所有这些因素，而不仅仅是代谢成本。最初的基本科学探索将发现健康人群中代谢成本的有效代理，并在中风后人类中发展多目标。在这两种情况下，目标函数都必须对外套件控制变化的衡量，准确和响应均无意义。第二个实施阶段将使用人类在环境优化的情况下自动适应可便携式系统的控制参数，以帮助健康的个人和人员后中暑。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来进行评估的。

项目成果

Real-time gait metric estimation for everyday gait training with wearable devices in people poststroke

• DOI: 10.1017/wtc.2020.11
• 发表时间: 2021-03-25
• 期刊: WEARABLE TECHNOLOGIES
• 作者: Arens, Philipp;Siviy, Christopher;Walsh, Conor J.
• 通讯作者: Walsh, Conor J.

Effects of a soft robotic exosuit on the quality and speed of overground walking depends on walking ability after stroke.

• DOI: 10.1186/s12984-023-01231-7
• 发表时间: 2023-09-01
• 期刊: JOURNAL OF NEUROENGINEERING AND REHABILITATION
• 影响因子: 5.1
• 作者: Sloot, Lizeth H.;Baker, Lauren M.;Bae, Jaehyun;Porciuncula, Franchino;Clement, Blandine F.;Siviy, Christopher;Nuckols, Richard W.;Baker, Teresa;Sloutsky, Regina;Choe, Dabin K.;O'Donnell, Kathleen;Ellis, Terry D.;Awad, Louis N.;Walsh, Conor J.
• 通讯作者: Walsh, Conor J.

Individualized Learning-Based Ground Reaction Force Estimation in People Post-Stroke Using Pressure Insoles

• DOI: 10.1109/icorr58425.2023.10304695
• 发表时间: 2023-09
• 期刊: 2023 International Conference on Rehabilitation Robotics (ICORR)
• 作者: Gregoire Bergamo;K. Swaminathan;Daekyum Kim;Andrew Chin;Christopher Siviy;Ignacio Novillo;Teresa C. Baker;Nicholas Wendel;Terry D. Ellis;Conor J. Walsh

Opportunities and challenges in the development of exoskeletons for locomotor assistance

• DOI: 10.1038/s41551-022-00984-1
• 发表时间: 2022-12-22
• 期刊: NATURE BIOMEDICAL ENGINEERING
• 影响因子: 28.1
• 作者: Siviy, Christopher;Baker, Lauren M. M.;Walsh, Conor J. J.
• 通讯作者: Walsh, Conor J. J.

Design and evaluation of an independent 4‐week, exosuit‐assisted, post‐stroke community walking program

• DOI: 10.1111/nyas.14998
• 发表时间: 2023-05
• 期刊: Annals of the New York Academy of Sciences
• 影响因子: 5.2
• 作者: R. Nuckols;Chih-Kang Chang;Daekyum Kim;A. Eckert-Erdheim;Dorothy Orzel;L. Baker;Teresa C. Baker;Nicholas Wendel;Brendan T. Quinlivan;Patrick Murphy;Jesse Grupper;Jacqueline Villalobos;L. Awad;T. Ellis;C. Walsh