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使用上肢力量肌电图估计用户施加的等长力/扭矩。

Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography.

作者信息

Sakr Maram, Jiang Xianta, Menon Carlo

机构信息

Menrva Research Group, Schools of Mechatronic Systems and Engineering Science, Simon Fraser University, Burnaby, BC, Canada.

出版信息

Front Robot AI. 2019 Nov 22;6:120. doi: 10.3389/frobt.2019.00120. eCollection 2019.

Abstract

Hand force estimation is critical for applications that involve physical human-machine interactions for force monitoring and machine control. Force Myography (FMG) is a potential technique to be used for estimating hand force/torque. The FMG signals reflect the volumetric changes in the arm muscles due to muscle contraction or expansion. This paper investigates the feasibility of employing force-sensing resistors (FSRs) worn on the arm to measure the FMG signals for isometric force/torque estimation. Nine participants were recruited in this study and were asked to exert isometric force along three perpendicular axes, torque about the same three axes, and force and torque simultaneously. During the tests, the isometric force and torque were measured using a 6-degree-of-freedom (DoF) (i.e., force in three axes and torque around the same axes) load cell for ground truth labels whereas the FMG signals were recorded using a total number of 60 FSRs, which were embedded into four bands worn on the different locations of the arm. A two-stage regression strategy was employed to enhance the performance of the FMG bands, where three regression algorithms including general regression neural network (GRNN), support vector regression (SVR), and random forest regression (RF) models were employed, respectively, in the first stage and GRNN was used in the second stage. Two cases were considered to explore the performance of the FMG bands in estimating: (1) 3-DoF force and 3-DoF torque at once and (2) 6-DoF force and torque. In addition, the impact of sensor placement and the spatial coverage of FMG measurements were studied. This preliminary investigation demonstrates promising potential of FMG to estimate multi-DoF isometric force/torque. Specifically, accuracies of 0.83 for the 3-DoF force, 0.84 for 3-DoF torque, and 0.77 for the combination of force and torque (6-DoF) regressions were obtained using the four bands on the arm in cross-trial evaluation.

摘要

手部力估计对于涉及人机物理交互以进行力监测和机器控制的应用至关重要。肌动电流图(FMG)是一种用于估计手部力/扭矩的潜在技术。FMG信号反映了由于肌肉收缩或舒张导致的手臂肌肉体积变化。本文研究了使用佩戴在手臂上的力敏电阻(FSR)来测量FMG信号以进行等长力/扭矩估计的可行性。本研究招募了九名参与者,要求他们沿三个垂直轴施加等长力、围绕相同的三个轴施加扭矩以及同时施加力和扭矩。在测试过程中,使用六自由度(DoF)(即三个轴上的力和围绕相同轴的扭矩)测力传感器测量等长力和扭矩以获取地面真值标签,而FMG信号则使用总共60个FSR进行记录,这些FSR被嵌入到佩戴在手臂不同位置的四个带子中。采用两阶段回归策略来提高FMG频段的性能,其中在第一阶段分别采用了包括广义回归神经网络(GRNN)、支持向量回归(SVR)和随机森林回归(RF)模型在内的三种回归算法,在第二阶段使用GRNN。考虑了两种情况来探究FMG频段在估计方面的性能:(1)一次估计三维力和三维扭矩以及(2)估计六维力和扭矩。此外,还研究了传感器放置和FMG测量的空间覆盖范围的影响。这项初步研究表明FMG在估计多自由度等长力/扭矩方面具有广阔的潜力。具体而言,在交叉试验评估中,使用手臂上的四个带子分别获得了三维力回归精度为0.83、三维扭矩回归精度为0.84以及力和扭矩组合(六维)回归精度为0.77。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e836/7805656/558a1cfb1743/frobt-06-00120-g0001.jpg

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