Controlling Robot-Assisted Locomotion with Extended Kalman Filter Estimates of Phase and Activity

使用扩展卡尔曼滤波器估计相位和活动来控制机器人辅助运动

• 批准号: 10328286
• 负责人: Robert D Gregg
• 金额: $ 8.69万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10328286
• 关键词: Address; American; Area; Behavior; Characteristics; Communities; Data; Device or Instrument Development; Doctor of Philosophy; Gait; Goals; Human; Kinetics; Leg; Length; Locomotion; Measures; Medical center; Michigan; Mission; Modeling; National Institute of Child Health and Human Development; Orthotic Devices; Parents; Phase; Program Development; Prosthesis; Public Health; Quality of life; Ramp; Research; Research Personnel; Robot; Signal Transduction; Speed; Universities; clinical application; design; disability; improved; kinematics; phase change; powered exoskeleton; powered prosthesis; rate of change; rehabilitation research; robot assistance; technology development; theories; time use; vector

PROJECT ABSTRACT This diversity supplement to the parent R01 project will address our overall goal—to model and control human locomotion over continuously varying tasks—by indirectly estimating a gait-state comprising (1) the phase variable, (2) the phase rate, and (3) a selection of task variables such as the step length and ramp inclination. The phase variable is a signal that monotonically increases during the natural progression each human stride, and the phase rate is the rate of change of the phase variable. Task variables represent the physical characteristics of a locomotion task, such as the ground’s inclination or the speed of the human. Grouped together in the gait-state, these variables parametrize human leg kinematics and kinetics through a gait- mapping, which must be learned from gait data. The central hypothesis of this project is that continuously varying activities can be tracked in real-time by using the theory of state estimation to estimate this gait-state vector indirectly through a comparison between measured human kinematics and the kinematic predictions of the gait-mapping; this estimate can then be used to either imitate (in the prosthetic case) or augment (in the orthotic case) the human’s behavior. In this research, PhD candidate Roberto Manuel Leonardo Medrano III, BS (Leo) will be advised by PI Robert Gregg, PhD and Co-I Elliott Rouse, PhD, who will help him develop as a leader/researcher throughout the course of this project at the University of Michigan (U-M).

项目摘要 这个对R 01项目的多样性补充将解决我们的总体目标--模拟和控制人类 在连续变化的任务上的运动-通过间接估计步态状态，包括：（1）相位 变量，（2）相位速率，以及（3）任务变量的选择，例如步长和斜坡倾斜度。 相位变量是在每个人类步幅的自然进展期间单调增加的信号， 相位速率是相位变量的变化速率。任务变量表示物理 运动任务的特征，例如地面的倾斜度或人的速度。分组 在步态状态下，这些变量一起通过步态参数化人腿运动学和动力学， 映射，这必须从步态数据中学习。这个项目的核心假设是， 利用状态估计理论对步态状态进行估计， 通过测量人体运动学和运动学预测之间的比较， 步态映射;然后，该估计可以用于模仿（在假肢情况下）或增强（在 矫形器的情况下）的人的行为。在这项研究中，博士候选人罗伯托·曼努埃尔列奥纳多·梅德拉诺 III，BS（狮子座）将由PI Robert Gregg，PhD和Co-I Elliott Rouse，PhD提供建议，他们将帮助他开发 在密歇根大学（U-M）的整个项目过程中担任领导者/研究员。