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Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation

The implementation of robotic systems for minimally invasive surgery and medical procedures is an active topic of research in recent years. One of the most common procedures is the palpation of soft tissues to identify their mechanical characteristics. In particular, it is very useful to identify th...

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Autores principales: Gutierrez-Giles, Alejandro, Padilla-Castañeda, Miguel A., Alvarez-Icaza, Luis, Gutierrez-Herrera, Enoch
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694668/
https://www.ncbi.nlm.nih.gov/pubmed/36433266
http://dx.doi.org/10.3390/s22228670
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author Gutierrez-Giles, Alejandro
Padilla-Castañeda, Miguel A.
Alvarez-Icaza, Luis
Gutierrez-Herrera, Enoch
author_facet Gutierrez-Giles, Alejandro
Padilla-Castañeda, Miguel A.
Alvarez-Icaza, Luis
Gutierrez-Herrera, Enoch
author_sort Gutierrez-Giles, Alejandro
collection PubMed
description The implementation of robotic systems for minimally invasive surgery and medical procedures is an active topic of research in recent years. One of the most common procedures is the palpation of soft tissues to identify their mechanical characteristics. In particular, it is very useful to identify the tissue’s stiffness or equivalently its elasticity coefficient. However, this identification relies on the existence of a force sensor or a tactile sensor mounted at the tip of the robot, as well as on measuring the robot velocity. For some applications it would be desirable to identify the biomechanical characteristics of soft tissues without the need for a force/tactile nor velocity sensors. An estimation of such quantities can be obtained by a model-based state observer for which the inputs are only the robot joint positions and its commanded joint torques. The estimated velocities and forces can then be employed for closed-loop force control, force reflection, and mechanical parameters estimation. In this work, a closed-loop force control is proposed based on the estimated contact forces to avoid any tissue damage. Then, the information from the estimated forces and velocities is used in a least squares estimator of the mechanical parameters. Moreover, the estimated biomechanical parameters are employed in a Bayesian classifier to provide further help for the physician to make a diagnosis. We have found that a combination of the parameters of both linear and nonlinear viscoelastic models provide better classification results: 0% misclassifications against 50% when using a linear model, and 3.12% when using only a nonlinear model, for the case in which the samples have very similar mechanical properties.
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spelling pubmed-96946682022-11-26 Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation Gutierrez-Giles, Alejandro Padilla-Castañeda, Miguel A. Alvarez-Icaza, Luis Gutierrez-Herrera, Enoch Sensors (Basel) Article The implementation of robotic systems for minimally invasive surgery and medical procedures is an active topic of research in recent years. One of the most common procedures is the palpation of soft tissues to identify their mechanical characteristics. In particular, it is very useful to identify the tissue’s stiffness or equivalently its elasticity coefficient. However, this identification relies on the existence of a force sensor or a tactile sensor mounted at the tip of the robot, as well as on measuring the robot velocity. For some applications it would be desirable to identify the biomechanical characteristics of soft tissues without the need for a force/tactile nor velocity sensors. An estimation of such quantities can be obtained by a model-based state observer for which the inputs are only the robot joint positions and its commanded joint torques. The estimated velocities and forces can then be employed for closed-loop force control, force reflection, and mechanical parameters estimation. In this work, a closed-loop force control is proposed based on the estimated contact forces to avoid any tissue damage. Then, the information from the estimated forces and velocities is used in a least squares estimator of the mechanical parameters. Moreover, the estimated biomechanical parameters are employed in a Bayesian classifier to provide further help for the physician to make a diagnosis. We have found that a combination of the parameters of both linear and nonlinear viscoelastic models provide better classification results: 0% misclassifications against 50% when using a linear model, and 3.12% when using only a nonlinear model, for the case in which the samples have very similar mechanical properties. MDPI 2022-11-10 /pmc/articles/PMC9694668/ /pubmed/36433266 http://dx.doi.org/10.3390/s22228670 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gutierrez-Giles, Alejandro
Padilla-Castañeda, Miguel A.
Alvarez-Icaza, Luis
Gutierrez-Herrera, Enoch
Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation
title Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation
title_full Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation
title_fullStr Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation
title_full_unstemmed Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation
title_short Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation
title_sort force-sensorless identification and classification of tissue biomechanical parameters for robot-assisted palpation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694668/
https://www.ncbi.nlm.nih.gov/pubmed/36433266
http://dx.doi.org/10.3390/s22228670
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