权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Collaborative Research: Enhancing Gait Dynamics via Physical Human-human and Human-Robot Interactions at the Hands

合作研究：通过人与人以及人与机器人之间的物理交互增强步态动力学

基本信息

批准号：
1762211
负责人：
Lena Ting
金额：
$ 37.29万
依托单位：
Emory University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1762211&HistoricalAwards=false
关键词：
Collaborative Research Enhancing Gait Dynamics

项目摘要

This project contributes to the understanding of how intelligent robotic systems can use a model of human intent, perception, and behavior to enhance human-robot cooperation, as may occur during rehabilitative gait training in physical therapy. The project seeks to understand how small forces applied to the hand can be used to alter clinically-relevant human gait patterns. The project is significant because it generates the experimental data and models of human gait and sensorimotor cognition needed to develop a robotic device that will interact physically and intuitively with individuals with mobility impairments to enhance their quality of life. By developing a robotic test-bed system and the models of human sensorimotor behavior that will guide its actions, this project will serve the national interest and advance the NSF mission to promote the progress of science and to advance the national health. The project takes a three-stage approach to advancing the objectives of the NSF's Mind, Machine and Motor Nexus (M3X) program, which are to understand how the human mind controls body movements during the manipulation of machines, and how machine response can shape and influence both the mindset and movements of the human user. First, the project team will use novel instrumented devices and motion capture technology to measure interactive human-to-human hand forces and the resulting gait motions that occur during the proposed therapeutic intervention. In Stage 2 they will use machine learning techniques to develop a model of the motor and cognitive transformations that occurred during the human-to-human experiments of Stage 1. Finally, the team will embed the models within a novel robotic test-bed that will implement physical human-robot interactions at the hands. Human-robot experiments will evaluate the ability of the test-bed to promote desired changes in clinically-relevant gait characteristics such as gait speed, step length and step cadence. Fundamental issues addressed by the project include: 1) identifying the relationships between hand forces, gait dynamics and perceived intents of the instructor (therapist) and student (patient) that arise during a simplified version of rehabilitative partner dance; 2) modeling those relationships so as to enable prediction of how small hand forces indirectly change human gait dynamics; and 3) developing a robotic gait coach capable of promoting desired changes in human gait dynamics. In addition, the project supports education and promotes diversity through innovative outreach activities that will engage teams of students and older adults from underrepresented communities in the conception, design and prototyping of mobility-related assistive technologies. The project outcomes may have long-term impact on the quality of life of millions of Americans with deficits of gait by developing a robotic system that can assist therapists in their efforts to improve patient fitness, mobility, and independence.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.

该项目有助于理解智能机器人系统如何使用人类意图，感知和行为模型来增强人机合作，就像在物理治疗中的康复步态训练期间可能发生的那样。该项目旨在了解施加在手上的小力如何用于改变临床相关的人类步态模式。该项目意义重大，因为它生成了开发机器人设备所需的人类步态和感觉运动认知的实验数据和模型，该设备将与行动障碍者进行物理和直观的互动，以提高他们的生活质量。通过开发机器人试验台系统和人类感觉运动行为模型来指导其行动，该项目将服务于国家利益，并推进NSF的使命，以促进科学进步和促进国民健康。该项目采用三阶段方法来推进NSF的Mind，Machine and Motor Nexus（M3 X）计划的目标，即了解人类思维如何在操纵机器时控制身体运动，以及机器响应如何塑造和影响人类用户的心态和运动。首先，项目团队将使用新型仪器设备和运动捕捉技术来测量交互式人与人之间的手部力量以及在拟议的治疗干预期间发生的步态运动。在第二阶段，他们将使用机器学习技术来开发第一阶段人与人之间实验中发生的运动和认知转变的模型。最后，该团队将把这些模型嵌入到一个新的机器人测试平台中，该平台将实现物理的人机交互。人机实验将评估测试台促进临床相关步态特征（如步态速度、步长和步频）发生预期变化的能力。本研究所要解决的基本问题包括：1）识别手部力量、步态动力学和教练感知意图之间的关系（治疗师）和学生（患者）之间的关系; 2）对这些关系进行建模，以便能够预测小的手部力量如何间接改变人类步态动力学;以及3）开发能够促进人类步态动力学的期望变化的机器人步态教练。此外，该项目通过创新的外联活动支持教育和促进多样性，这些活动将使来自代表性不足社区的学生和老年人团队参与与行动有关的辅助技术的概念、设计和原型制作。该项目的成果可能会对数百万美国人的生活质量产生长期的影响，通过开发一个机器人系统，可以帮助治疗师在他们的努力，以改善病人的健身，流动性和独立性。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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）

Identifying Gait Phases from Joint Kinematics during Walking with Switched Linear Dynamical Systems*

使用切换线性动力系统*行走期间根据关节运动学识别步态阶段

DOI：
10.1109/biorob.2018.8487216
发表时间：
2018
期刊：
2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob
影响因子：
0
作者：
Drnach, Luke;Essa, Irfan;Ting, Lena H.
通讯作者：
Ting, Lena H.

Maximum Spectral Flatness Control of a Manipulandum for Human Motor System Identification

用于人体运动系统识别的操作器的最大光谱平坦度控制