NRI: Task-Based Assistance for Software-Enabled Biomedical Devices

NRI：针对软件支持的生物医学设备的基于任务的援助

• 批准号: 1637764
• 负责人: Todd Murphey
• 金额: $ 42.98万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637764&HistoricalAwards=false
• 关键词: NRI; Task; Based; Assistance; Software

1637764 - MurpheyRobotic assistive devices help people execute and learn physical tasks. Sometimes these tasks are relatively simple, sometimes they are in a particular context, and sometimes they are highly dynamic and very task specific. This work will create algorithms that enable the delivered assistance to take into account algorithmic descriptions of the underlying task. As an example, walking is a highly structured task that simultaneously requires efficiency and stability during motion and must take into account terrain characteristics. The work takes advantage of task knowledge to either modify a person's motion or exert forces that help the person complete the task. This capability is relevant to rehabilitation and physical therapy, where one may wish to only minimally help a person in order to improve therapy outcomes. This work will therefore impact the development of software that supports people engaged in robot-assisted physical therapy, including people recovering from various forms of injury. The key to this work is that knowledge of a task is combined with knowledge of a person's capabilities to synthesize software decisions that ensure safety while also maximizing a person's agency during motion. Broader impact of this work includes technology transfer to rehabilitation, outreach through the Museum of Science and Industry in Chicago, classroom innovation, and industry collaboration.The proposed work will create software-enabled, task-specific support for assistive biomedical devices. Dynamic tasks require that a combination of the robot and the assisted person be both effective and safe, and the proposed research will create algorithms and software that ensure efficacy and safety while leaving the user free to both move and exert effort. The latter is important in contexts like physical therapy, where effort is important to therapeutic impact. The proposed work will leverage recent results in real-time nonlinear optimal control techniques for human-in-the-loop systems. Specifically, sequential action control (SAC) will be used to both filter and assist human subject dynamic behavior, using a method called the Maxwell's Demon Algorithm. The work will additionally develop formal methodologies for establishing stability and performance guarantees for the proposed algorithms. Lastly, the proposed work will develop compact representations of the controlled assistance algorithms appropriate for computationally minimal embedded systems. All the work will be developed in the Robot Operating System (ROS), making the developed tools widely available to both researchers and companies. The algorithms will be tested on haptic devices and an exoskeleton. The broader impacts for this work will include outreach, technology transfer to rehabilitation, the development of courses in dynamics and analysis, and industrial collaboration. The PI is currently working with the Museum of Science and Industry, and as part of the proposed work the PI and supported students will participate in a National Robotics Week exhibit in the main rotunda of the museum with an estimated viewership of over ten thousand on-site visitors. The PI is involved in significant classroom innovations, and the proposed work will include development of courses in analysis and dynamics. Lastly, the project will include a collaboration with Ekso Bionics, leveraging and impacting their unparalleled expertise in exoskeleton development.

1637764 -Murphey机器人辅助设备帮助人们执行和学习物理任务。有时这些任务相对简单，有时它们在特定的环境中，有时它们是高度动态的，并且非常特定于任务。这项工作将创建算法，使交付的援助，以考虑到算法描述的基础任务。例如，步行是一项高度结构化的任务，在运动过程中同时需要效率和稳定性，并且必须考虑地形特征。这项工作利用任务知识来修改一个人的动作或施加力量帮助这个人完成任务。这种能力与康复和物理治疗有关，其中人们可能希望仅最低限度地帮助一个人以改善治疗结果。因此，这项工作将影响软件的开发，支持从事机器人辅助物理治疗的人，包括从各种形式的伤害中恢复的人。这项工作的关键是，将任务的知识与人的能力相结合，以综合软件决策，确保安全，同时最大限度地提高人在运动过程中的代理能力。这项工作的更广泛的影响包括技术转移到康复，通过科学和工业博物馆在芝加哥，课堂创新和行业合作的推广。拟议的工作将创建软件启用，特定任务的辅助生物医学设备的支持。动态任务要求机器人和辅助人员的组合既有效又安全，拟议的研究将创建算法和软件，以确保有效性和安全性，同时让用户自由移动和努力。后者在物理治疗等情况下很重要，努力对治疗效果很重要。拟议的工作将利用最近的成果，在实时非线性最优控制技术的人在回路系统。具体来说，顺序动作控制（SAC）将被用来过滤和协助人类主体的动态行为，使用一种称为麦克斯韦的恶魔算法的方法。这项工作还将开发正式的方法，为拟议的算法建立稳定性和性能保证。最后，所提出的工作将开发紧凑的表示控制辅助算法适合计算最小的嵌入式系统。所有的工作都将在机器人操作系统（ROS）中开发，使开发的工具广泛适用于研究人员和公司。这些算法将在触觉设备和外骨骼上进行测试。这项工作的更广泛影响将包括外联、向康复转让技术、编制动态和分析课程以及工业合作。PI目前正在与科学和工业博物馆合作，作为拟议工作的一部分，PI和支持的学生将参加在博物馆主圆形大厅举行的国家机器人周展览，估计现场观众超过一万人。PI参与了重大的课堂创新，拟议的工作将包括开发分析和动态课程。最后，该项目将包括与Ekso Bionics的合作，利用和影响他们在外骨骼开发方面无与伦比的专业知识。

项目成果

Data-Driven Gait Segmentation for Walking Assistance in a Lower-Limb Assistive Device

• DOI: 10.1109/icra.2019.8794416
• 发表时间: 2019-02
• 期刊: 2019 International Conference on Robotics and Automation (ICRA)
• 作者: A. Kalinowska;Thomas A. Berrueta;A. Zoss;T. Murphey

Task-based hybrid shared control for training through forceful interaction

基于任务的混合共享控制，通过强制交互进行训练

• DOI: 10.1177/0278364920933654
• 发表时间: 2020
• 期刊: The International Journal of Robotics Research
• 作者: Fitzsimons, Kathleen;Kalinowska, Aleksandra;Dewald, Julius P;Murphey, Todd D
• 通讯作者: Murphey, Todd D

Online User Assessment for Minimal Intervention During Task-Based Robotic Assistance

在基于任务的机器人协助期间进行最小干预的在线用户评估

• DOI: 10.15607/rss.2018.xiv.046
• 发表时间: 2018
• 期刊: Robotics: science and systems
• 作者: Kalinowska, Aleksandra;Fitzsimons, Kathleen;Dewald, Julius;Murphey, Todd
• 通讯作者: Murphey, Todd

Shoulder abduction loading affects motor coordination in individuals with chronic stroke, informing targeted rehabilitation

肩外展负荷影响慢性中风患者的运动协调性，为有针对性的康复提供信息

• DOI: 10.1109/biorob49111.2020.9224454
• 发表时间: 2020
• 期刊: 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob
• 作者: Kalinowska, Aleksandra;Rudy, Kyra;Schlafly, Millicent;Fitzsimons, Kathleen;Dewald, Julius P;Murphey, Todd D
• 通讯作者: Murphey, Todd D

Ergodicity reveals assistance and learning from physical human-robot interaction

• DOI: 10.1126/scirobotics.aav6079
• 发表时间: 2019-04-24
• 期刊: SCIENCE ROBOTICS
• 影响因子: 25
• 作者: Fitzsimons, Kathleen;Acosta, Ana Maria;Murphey, Todd D.
• 通讯作者: Murphey, Todd D.