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.

该项目将推动可定制的软机器人外衣的进展，以便在日常活动中提供无缝帮助，减轻损伤或增强正常功能。柔软的机器人外衣是一种新型的功能性服装，它在穿戴者的肌肉的同时，对他们的关节进行机械辅助。数以百万计的美国人患有神经性步行障碍，比如中风后生活的近700万人，他们可以受益于外衣在步行过程中提供的不引人注目的帮助。同样，长距离携带重物的健康个人(例如，急救员或士兵)可以从使用Exosuit技术部分减轻他们的负担中受益。使机械辅助与佩戴者的自然节奏同步，对于实现这些设备的潜力至关重要。该项目中创建的软机器人外衣将使用穿戴在身上的传感器来监控穿戴者的行走模式，应用机器学习方法来个性化机器人辅助模式，并随着穿戴者步态的变化不断更新辅助模式。相比之下，目前的方法依赖于专业的临床医生和技术人员来手动调整辅助模式。该项目将为促进国民健康和繁荣贡献新的知识。多学科研究团队包括机器人专家、运动学家和临床医生，他们将在实验室和临床环境中与中风后患者密切合作。最近开发的外骨骼人在环中优化策略表明，将外骨骼控制视为一个优化问题，以直接测量代谢成本为目标，有可能降低步行的代谢成本。然而，记录这样的测量需要笨重的设备来干扰正常的呼吸，这意味着这种方法目前不能在日常环境中工作。此外，降低步行的代谢成本并不是中风后穿外衣的唯一目标，中风后患者的步态特征是缓慢运动和代偿，如髋部徒步、绕圈和跳跃。在临床人群中成功的人在环路优化将需要解决所有这些因素，而不仅仅是代谢成本。初步的基础科学探索将在健康人群中找到有效的代谢成本指标，并在中风后人群中开发多目标函数。在这两种情况下，目标函数必须不显眼地测量，准确，并对外衣控制的变化作出反应。第二个实施阶段将使用人在回路中的优化来自动调整便携式系统的控制参数，以帮助健康的个人和中风后的人。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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

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.

Ankle-targeted exosuit resistance increases paretic propulsion in people post-stroke.

• DOI: 10.1186/s12984-023-01204-w
• 发表时间: 2023-06-30
• 期刊: JOURNAL OF NEUROENGINEERING AND REHABILITATION
• 影响因子: 5.1
• 作者: Swaminathan, Krithika;Porciuncula, Franchino;Park, Sungwoo;Kannan, Harini;Erard, Julien;Wendel, Nicholas;Baker, Teresa;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