Literature DB >> 33501135

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

Maram Sakr1, Xianta Jiang1, Carlo Menon1.   

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, R 2 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.
Copyright © 2019 Sakr, Jiang and Menon.

Entities:  

Keywords:  force myography; hand force/torque estimation; human-machine interaction; multi-output regression; wearable sensors

Year:  2019        PMID: 33501135      PMCID: PMC7805656          DOI: 10.3389/frobt.2019.00120

Source DB:  PubMed          Journal:  Front Robot AI        ISSN: 2296-9144


  21 in total

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Authors:  Mathilde Connan; Eduardo Ruiz Ramírez; Bernhard Vodermayer; Claudio Castellini
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  2 in total

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Review 2.  A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human-Machine Interactivities and Biomedical Applications.

Authors:  Zhuo Zheng; Zinan Wu; Runkun Zhao; Yinghui Ni; Xutian Jing; Shuo Gao
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  2 in total

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