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

• 批准号: 1761679
• 负责人: Jun Ueda
• 金额: $ 31.92万
• 依托单位: Georgia Tech Research Corporation
• 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 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1761679&HistoricalAwards=false
• 关键词: Collaborative; Research; Enhancing; Gait; Dynamics

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，这可能发生在物理治疗中的康复步态训练中。该项目旨在了解如何利用施加在手上的微小力量来改变临床相关的人类步态模式。该项目意义重大，因为它生成了人类步态和感觉运动认知的实验数据和模型，需要开发一种机器人设备，可以与行动不便的人进行物理和直观的互动，以提高他们的生活质量。通过开发机器人测试平台系统和指导其行动的人类感觉运动行为模型，该项目将服务于国家利益，并推进NSF促进科学进步和促进国民健康的使命。该项目采取了三个阶段的方法来推进NSF的心智、机器和运动Nexus （M3X）项目的目标，即了解在操纵机器过程中人类的思想如何控制身体运动，以及机器的反应如何塑造和影响人类用户的思维和运动。首先，项目团队将使用新型仪器设备和动作捕捉技术来测量在拟议的治疗干预期间发生的人机交互的手部力量和由此产生的步态运动。在第二阶段，他们将使用机器学习技术来开发在第一阶段的人对人实验中发生的运动和认知转换的模型。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，该平台将在手上实现物理的人机交互。人-机器人实验将评估试验台促进临床相关步态特征（如步态速度、步长和步伐节奏）所需变化的能力。该项目解决的基本问题包括：1)确定在简化版本的康复舞伴舞蹈中出现的手部力量，步态动力学和教练（治疗师）和学生（患者）的感知意图之间的关系；2)建立这些关系的模型，从而能够预测微小的手部力量如何间接改变人类的步态动力学；3)开发一种机器人步态教练，能够促进人类步态动力学的预期变化。此外，该项目还通过创新的外展活动支持教育和促进多样性，这些活动将吸引来自代表性不足社区的学生和老年人团队参与与移动相关的辅助技术的概念、设计和原型制作。通过开发机器人系统，该项目成果可能会对数百万步态缺陷的美国人的生活质量产生长期影响，该系统可以帮助治疗师努力改善患者的健康，活动能力和独立性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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

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
• 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

• DOI: 10.1109/lra.2021.3063964
• 发表时间: 2021
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Qiu, Yingxin;Wu, Mengnan;Ting, Lena H.;Ueda, Jun
• 通讯作者: Ueda, Jun

Optimal Multisine Perturbations for Improved Dynamic System Identification using a Mechanical Platform: A Preliminary Simulation Study

使用机械平台改进动态系统识别的最佳多重正弦扰动：初步仿真研究

• DOI: 10.1109/aim46323.2023.10196176
• 发表时间: 2023
• 期刊: IEEE
• 作者: Qiu, Yingxin;Wu, Mengnan;Ting, Lena H.;Ueda, Jun
• 通讯作者: Ueda, Jun

Ask this robot for a helping hand

请求这个机器人伸出援手

• DOI: 10.1038/s42256-018-0013-0
• 发表时间: 2019
• 期刊: Nature Machine Intelligence
• 影响因子: 23.8
• 作者: Drnach, Luke;Ting, Lena H.
• 通讯作者: Ting, Lena H.

A Versatile Emulator for Haptic Communication to Alter Human Gait Parameters

用于改变人类步态参数的触觉通信的多功能模拟器

• DOI: 10.1109/biorob52689.2022.9925268
• 发表时间: 2022
• 期刊: IEEE
• 作者: Wu, Mengnan;Qiu, Yingxin;Ueda, Jun;Ting, Lena H.
• 通讯作者: Ting, Lena H.