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Artificial Skin Ridges Enhance Local Tactile Shape Discrimination
One of the fundamental requirements for an artificial hand to successfully grasp and manipulate an object is to be able to distinguish different objects’ shapes and, more specifically, the objects’ surface curvatures. In this study, we investigate the possibility of enhancing the curvature detection...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231512/ https://www.ncbi.nlm.nih.gov/pubmed/22164095 http://dx.doi.org/10.3390/s110908626 |
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author | Salehi, Saba Cabibihan, John-John Ge, Shuzhi Sam |
author_facet | Salehi, Saba Cabibihan, John-John Ge, Shuzhi Sam |
author_sort | Salehi, Saba |
collection | PubMed |
description | One of the fundamental requirements for an artificial hand to successfully grasp and manipulate an object is to be able to distinguish different objects’ shapes and, more specifically, the objects’ surface curvatures. In this study, we investigate the possibility of enhancing the curvature detection of embedded tactile sensors by proposing a ridged fingertip structure, simulating human fingerprints. In addition, a curvature detection approach based on machine learning methods is proposed to provide the embedded sensors with the ability to discriminate the surface curvature of different objects. For this purpose, a set of experiments were carried out to collect tactile signals from a 2 × 2 tactile sensor array, then the signals were processed and used for learning algorithms. To achieve the best possible performance for our machine learning approach, three different learning algorithms of Naïve Bayes (NB), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) were implemented and compared for various parameters. Finally, the most accurate method was selected to evaluate the proposed skin structure in recognition of three different curvatures. The results showed an accuracy rate of 97.5% in surface curvature discrimination. |
format | Online Article Text |
id | pubmed-3231512 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-32315122011-12-07 Artificial Skin Ridges Enhance Local Tactile Shape Discrimination Salehi, Saba Cabibihan, John-John Ge, Shuzhi Sam Sensors (Basel) Article One of the fundamental requirements for an artificial hand to successfully grasp and manipulate an object is to be able to distinguish different objects’ shapes and, more specifically, the objects’ surface curvatures. In this study, we investigate the possibility of enhancing the curvature detection of embedded tactile sensors by proposing a ridged fingertip structure, simulating human fingerprints. In addition, a curvature detection approach based on machine learning methods is proposed to provide the embedded sensors with the ability to discriminate the surface curvature of different objects. For this purpose, a set of experiments were carried out to collect tactile signals from a 2 × 2 tactile sensor array, then the signals were processed and used for learning algorithms. To achieve the best possible performance for our machine learning approach, three different learning algorithms of Naïve Bayes (NB), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) were implemented and compared for various parameters. Finally, the most accurate method was selected to evaluate the proposed skin structure in recognition of three different curvatures. The results showed an accuracy rate of 97.5% in surface curvature discrimination. Molecular Diversity Preservation International (MDPI) 2011-09-05 /pmc/articles/PMC3231512/ /pubmed/22164095 http://dx.doi.org/10.3390/s110908626 Text en © 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Salehi, Saba Cabibihan, John-John Ge, Shuzhi Sam Artificial Skin Ridges Enhance Local Tactile Shape Discrimination |
title | Artificial Skin Ridges Enhance Local Tactile Shape Discrimination |
title_full | Artificial Skin Ridges Enhance Local Tactile Shape Discrimination |
title_fullStr | Artificial Skin Ridges Enhance Local Tactile Shape Discrimination |
title_full_unstemmed | Artificial Skin Ridges Enhance Local Tactile Shape Discrimination |
title_short | Artificial Skin Ridges Enhance Local Tactile Shape Discrimination |
title_sort | artificial skin ridges enhance local tactile shape discrimination |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231512/ https://www.ncbi.nlm.nih.gov/pubmed/22164095 http://dx.doi.org/10.3390/s110908626 |
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