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

基本信息

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

项目摘要

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体力任务。有时这些任务相对简单，有时它们处于特定的上下文中，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，使所提供的帮助能够考虑到底层任务的算法描述。例如，步行是一项高度结构化的任务，同时要求运动过程中的效率和稳定性，并且必须考虑地形特征。这项工作利用任务知识来改变人的动作或施加力量来帮助人完成任务。这种能力与康复和物理治疗相关，人们可能希望只提供最低限度的帮助来改善治疗结果。因此，这项工作将影响支持人们从事机器人辅助物理治疗的软件的开发，包括从各种形式的伤害中恢复的人们。这项工作的关键在于，将任务知识与人的能力知识相结合，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能动性。这项工作的更广泛影响包括康复技术转让、通过芝加哥科学与工业博物馆进行推广、课堂创新和行业合作。拟议的工作将为辅助生物医学设备创建基于软件的、针对特定任务的支持。动态任务要求机器人和被协助者的组合既有效又安全，拟议的研究将创建算法和软件，确保有效性和安全性，同时让用户自由移动和努力。后者在物理治疗等环境中很重要，因为在物理治疗中，努力对于治疗效果很重要。拟议的工作将利用人在环系统实时非线性最优控制技术的最新成果。具体来说，顺序动作控制（SAC）将使用一种称为麦克斯韦恶魔算法的方法来过滤和辅助人类主体的动态行为。这项工作还将开发正式的方法来为所提出的算法建立稳定性和性能保证。最后，所提出的工作将开发适用于计算最小嵌入式系统的受控辅助算法的紧凑表示。所有工作都将在机器人操作系统（ROS）中开发，使开发的工具广泛可供研究人员和公司使用。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展、康复技术转让、动力学和分析课程的开发以及行业合作。 PI 目前正在与科学与工业博物馆合作，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，预计观众人数超过一万人。网站访问者。 PI 参与了重大的课堂创新，拟议的工作将包括分析和动力学课程的开发。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

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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：
2018-06
期刊：
Robotics: science and systems
影响因子：
0
作者：
Kalinowska, Aleksandra;Fitzsimons, Kathleen;Dewald, Julius;Murphey, Todd
通讯作者：
Murphey, Todd

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

用于下肢辅助设备步行辅助的数据驱动步态分割

DOI：
10.1109/icra.2019.8794416
发表时间：
2019-02-28
期刊：
2019 International Conference on Robotics and Automation (ICRA)
影响因子：
0
作者：
A. Kalinowska;Thomas A. Berrueta;A. Zoss;T. Murphey
通讯作者：
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：
2020-06
期刊：
The International Journal of Robotics Research
影响因子：
0
作者：
Fitzsimons, Kathleen;Kalinowska, Aleksandra;Dewald, Julius P;Murphey, Todd D
通讯作者：
Murphey, Todd D

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：
2020-11
期刊：
2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob
影响因子：
0
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

遍历性揭示了人机物理交互的帮助和学习