Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Sakr, Maram, Jiang, Xianta, Menon, Carlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805656/
https://www.ncbi.nlm.nih.gov/pubmed/33501135
http://dx.doi.org/10.3389/frobt.2019.00120
_version_ 1783636349880893440
author Sakr, Maram
Jiang, Xianta
Menon, Carlo
author_facet Sakr, Maram
Jiang, Xianta
Menon, Carlo
author_sort Sakr, Maram
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7805656
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-78056562021-01-25 Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography Sakr, Maram Jiang, Xianta Menon, Carlo Front Robot AI Robotics and AI 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. Frontiers Media S.A. 2019-11-22 /pmc/articles/PMC7805656/ /pubmed/33501135 http://dx.doi.org/10.3389/frobt.2019.00120 Text en Copyright © 2019 Sakr, Jiang and Menon. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Sakr, Maram
Jiang, Xianta
Menon, Carlo
Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography
title Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography
title_full Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography
title_fullStr Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography
title_full_unstemmed Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography
title_short Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography
title_sort estimation of user-applied isometric force/torque using upper extremity force myography
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805656/
https://www.ncbi.nlm.nih.gov/pubmed/33501135
http://dx.doi.org/10.3389/frobt.2019.00120
work_keys_str_mv AT sakrmaram estimationofuserappliedisometricforcetorqueusingupperextremityforcemyography
AT jiangxianta estimationofuserappliedisometricforcetorqueusingupperextremityforcemyography
AT menoncarlo estimationofuserappliedisometricforcetorqueusingupperextremityforcemyography