User-friendly IMU-based Human Motion Measurement

用户友好的基于 IMU 的人体运动测量

基本信息

  • 批准号:
    1805896
  • 负责人:
  • 金额:
    $ 32.99万
  • 依托单位:
  • 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:
    2018-08-01 至 2022-07-31
  • 项目状态:
    已结题

项目摘要

PI: Fregly, Benjamin J.Proposal: 1805896Stroke, Parkinson's disease, and osteoarthritis affect roughly 15% of the U.S. adult population and often impair walking ability, leading to an increased risk of serious health conditions and a decreased quality of life. Traditional approaches for assessing patient function and delivering rehabilitation prescriptions require that the patient make repeated visits to the clinic, which is expensive and time consuming for the patient, the clinician, and the health care system. Ideally, clinicians could gather quantitative information about patient walking function, as well as deliver rehabilitation prescriptions, when the patient is at home or in the community. Wireless inertial measurement units (IMUs), which are electronic devices that use accelerometers to measure linear acceleration (along a straight line) and gyroscopes to measure angular velocity (rotation), are already being explored to provide such capabilities. However, existing computational algorithms (problem solving instructions) that convert IMU measurements into joint angle measurements are inadequate to support an IMU-based motion measurement system that provides accurate joint motion measurements AND is so simple that patients, family members, or caregivers can use it reliably with no technical expertise. This project seeks to develop a novel computational algorithm that achieves these goals. The algorithm will achieve accuracy and simplicity by combining several unique computational approaches. The proposed project will 1) DEVELOP the necessary computational algorithm using preexisting motion capture data collected simultaneously, 2) EVALUATE the algorithm's ease of use and accuracy by having 10 healthy subjects collect their own IMU-based motion capture data, and 3) DEPLOY the algorithm on a Windows tablet or Mac laptop to demonstrate that it can be used for real-time feedback applications. If successful, this project could facilitate the development of effective remote monitoring and telerehabilitation methods that reduce the need for clinician time and patient office visits, decrease healthcare costs, and increase treatment effectiveness. For outreach, "at risk" students in a Houston middle school will interact with the technology firsthand by competing in an annual "Jump Off" competition held via the internet (Skype) with students in a sister middle school in Auckland, New Zealand.The goal of this project is to develop a wireless user-friendly IMU-based motion measurement system that is so simple and easy to use that virtually any patient or caregiver can use it to measure full-body walking movements at home or in the community with little training. Such a system could be clinically beneficial for both remote assessment of patient function using logged data and remote delivery of rehabilitation prescriptions using real-time feedback. The core of the stem is a novel motion estimation algorithm that will work for data logging and real-time applications. The algorithm will require only 6 IMUs-- each foot, pelvis, torso and each wrist--to measure full-body walking kinematics easily and repeatably. The project will progress in three phases. Phase 1 will involve algorithm development. The algorithm will be an expanded version of an unscented filter (UF) algorithm developed for studying an existing planar 6 DOF walking model. The first task will be to make that model more general and flexible by adding more coordinates and extending it to 3D. The algorithm will be tested using experimental walking data and data obtained from an existing 29 DOF walking model representative of real-life conditions. Phase 2 will involve algorithm evaluation for off-line applications. To demonstrate that, with minimum training, individuals in any environment can collect their own kinematic walking data using the algorithm, 10 healthy subjects will collect their own IMU-based walking data while also wearing markers to enable simultaneous marker-based motion capture to validate results. Phase 3 will involve algorithm deployment for real-time applications. The computational speed of the algorithm when deployed on hardware for real-time applications will be evaluated. A wireless IMU based motion feedback system, consisting of 6 wireless IMUs and a Windows (or Mac) tablet control unit with software for real-time calculation and display of joint angles during walking, will be developed. To verify that the system meets the project's goals --ease of use, rapid calibration, and collection and display of calculated joint positions in real time, 3 additional subjects will perform real-time data collection on themselves without wearing markers.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.
主要研究者:Fregly,Benjamin J.Proposal:1805896中风、帕金森病和骨关节炎影响约15%的美国成年人,通常会损害行走能力,导致严重健康状况的风险增加和生活质量下降。用于评估患者功能和递送康复处方的传统方法需要患者重复访问诊所,这对于患者、临床医生和医疗保健系统来说是昂贵且耗时的。理想情况下,临床医生可以收集有关患者步行功能的定量信息,并在患者在家或社区时提供康复处方。无线惯性测量单元(伊穆斯)是使用加速度计来测量线性加速度(沿着直线)和陀螺仪来测量角速度(旋转)的电子设备,其已经被探索以提供这样的能力。然而,将IMU测量值转换为关节角度测量值的现有计算算法(问题解决指令)不足以支持基于IMU的运动测量系统,该运动测量系统提供准确的关节运动测量值并且非常简单,使得患者、家庭成员或护理人员可以在没有技术专长的情况下可靠地使用它。该项目旨在开发一种新的计算算法来实现这些目标。该算法将通过结合几种独特的计算方法来实现准确性和简单性。拟议的项目将1)使用同时收集的预先存在的运动捕捉数据来验证必要的计算算法,2)通过让10名健康受试者收集他们自己的基于IMU的运动捕捉数据来验证算法的易用性和准确性,以及3)在Windows平板电脑或Mac笔记本电脑上部署算法,以证明它可以用于实时反馈应用。如果成功,该项目可以促进有效的远程监测和远程康复方法的开发,减少临床医生的时间和患者的办公室访问,降低医疗成本,并提高治疗效果。为了推广,休斯顿一所中学的“有风险”的学生将通过与奥克兰一所姐妹中学的学生通过互联网(Skype)举行的年度“跳下”比赛,直接与这项技术互动,该项目的目标是开发一种无线用户友好的惯性测量装置,基于运动测量系统,简单易用,几乎任何患者或护理人员都可以使用它来测量全身步行运动在家里或在社区很少的培训。这样的系统在临床上对于使用记录的数据的患者功能的远程评估和使用实时反馈的康复处方的远程递送都是有益的。干的核心是一种新的运动估计算法,将工作的数据记录和实时应用。该算法只需要6个伊穆斯--每只脚、骨盆、躯干和每个手腕--就可以轻松、可重复地测量全身步行运动学。该项目将分三个阶段进行。第一阶段将涉及算法开发。 该算法将是一个扩展版本的无迹滤波器(UF)算法研究现有的平面6自由度步行模型。 第一项任务是通过添加更多坐标并将其扩展到3D,使该模型更加通用和灵活。 该算法将使用实验行走数据和从代表现实生活条件的现有29自由度行走模型获得的数据进行测试。 第2阶段将涉及离线应用的算法评估。为了证明,通过最少的训练,任何环境中的个人都可以使用该算法收集他们自己的运动学行走数据,10名健康受试者将收集他们自己的基于IMU的行走数据,同时还佩戴标记,以实现同步的基于标记的运动捕获来验证结果。 第3阶段将涉及实时应用程序的算法部署。 该算法的计算速度时,部署在硬件上的实时应用程序将进行评估。 将开发一种基于无线IMU的运动反馈系统,该系统由6个无线伊穆斯和一个Windows(或Mac)平板电脑控制单元组成,该控制单元带有用于在行走期间实时计算和显示关节角度的软件。 为了验证该系统是否满足项目的目标--易用性、快速校准以及真实的实时收集和显示计算出的关节位置,另外3名受试者将在不佩戴标记的情况下进行实时数据收集。该奖项反映了NSF的法定使命,并通过使用基金会的智力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

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Evaluation of an Optimization Method for Measuring Treadmill Walking Motion using IMUs
使用 IMU 测量跑步机行走运动的优化方法的评估
EVALUATING AN OPTIMIZATION METHOD TO MEASURE TREADMILL AND OVERGROUND WALKING MOTIONS USING IMUS
评估使用 IMU 测量跑步机和地面行走运动的优化方法
Evaluation of a Nonlinear Optimization Method for Measuring Human Movement Using Inertial Measurement Units
使用惯性测量单元测量人体运动的非线性优化方法的评估
Optimization vs Unscented Filtering for Measuring Walking Motion using IMUs
使用 IMU 测量步行运动的优化与无味过滤
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Benjamin Fregly其他文献

Benjamin Fregly的其他文献

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{{ truncateString('Benjamin Fregly', 18)}}的其他基金

Computational Neuromechanics for Stroke Rehabilitation
中风康复的计算神经力学
  • 批准号:
    1159735
  • 财政年份:
    2012
  • 资助金额:
    $ 32.99万
  • 项目类别:
    Standard Grant
EAGER: Treatment Planning for Gait Pathologies Based on Whole-Body Angular and Linear Momentum
EAGER:基于全身角动量和线性动量的步态病理治疗计划
  • 批准号:
    1052754
  • 财政年份:
    2010
  • 资助金额:
    $ 32.99万
  • 项目类别:
    Standard Grant
Computational Simulation of Knee Osteoarthritis Development
膝骨关节炎发展的计算模拟
  • 批准号:
    0828253
  • 财政年份:
    2009
  • 资助金额:
    $ 32.99万
  • 项目类别:
    Standard Grant
Surrogate-Based Modeling of Joint Contact Mechanics
基于代理的关节接触力学建模
  • 批准号:
    0602996
  • 财政年份:
    2006
  • 资助金额:
    $ 32.99万
  • 项目类别:
    Standard Grant
CAREER: Virtual Prototyping of Artificial Knees
职业:人工膝关节虚拟原型设计
  • 批准号:
    0239042
  • 财政年份:
    2003
  • 资助金额:
    $ 32.99万
  • 项目类别:
    Continuing Grant

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