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Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms
Assistive technologies help all persons with disabilities to improve their accessibility in all aspects of their life. The AIDE European project contributes to the improvement of current assistive technologies by developing and testing a modular and adaptive multimodal interface customizable to the...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111334/ https://www.ncbi.nlm.nih.gov/pubmed/30042372 http://dx.doi.org/10.3390/s18082408 |
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author | Ivorra, Eugenio Ortega, Mario Catalán, José M. Ezquerro, Santiago Lledó, Luis Daniel Garcia-Aracil, Nicolás Alcañiz, Mariano |
author_facet | Ivorra, Eugenio Ortega, Mario Catalán, José M. Ezquerro, Santiago Lledó, Luis Daniel Garcia-Aracil, Nicolás Alcañiz, Mariano |
author_sort | Ivorra, Eugenio |
collection | PubMed |
description | Assistive technologies help all persons with disabilities to improve their accessibility in all aspects of their life. The AIDE European project contributes to the improvement of current assistive technologies by developing and testing a modular and adaptive multimodal interface customizable to the individual needs of people with disabilities. This paper describes the computer vision algorithms part of the multimodal interface developed inside the AIDE European project. The main contribution of this computer vision part is the integration with the robotic system and with the other sensory systems (electrooculography (EOG) and electroencephalography (EEG)). The technical achievements solved herein are the algorithm for the selection of objects using the gaze, and especially the state-of-the-art algorithm for the efficient detection and pose estimation of textureless objects. These algorithms were tested in real conditions, and were thoroughly evaluated both qualitatively and quantitatively. The experimental results of the object selection algorithm were excellent (object selection over 90%) in less than 12 s. The detection and pose estimation algorithms evaluated using the LINEMOD database were similar to the state-of-the-art method, and were the most computationally efficient. |
format | Online Article Text |
id | pubmed-6111334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61113342018-08-30 Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms Ivorra, Eugenio Ortega, Mario Catalán, José M. Ezquerro, Santiago Lledó, Luis Daniel Garcia-Aracil, Nicolás Alcañiz, Mariano Sensors (Basel) Article Assistive technologies help all persons with disabilities to improve their accessibility in all aspects of their life. The AIDE European project contributes to the improvement of current assistive technologies by developing and testing a modular and adaptive multimodal interface customizable to the individual needs of people with disabilities. This paper describes the computer vision algorithms part of the multimodal interface developed inside the AIDE European project. The main contribution of this computer vision part is the integration with the robotic system and with the other sensory systems (electrooculography (EOG) and electroencephalography (EEG)). The technical achievements solved herein are the algorithm for the selection of objects using the gaze, and especially the state-of-the-art algorithm for the efficient detection and pose estimation of textureless objects. These algorithms were tested in real conditions, and were thoroughly evaluated both qualitatively and quantitatively. The experimental results of the object selection algorithm were excellent (object selection over 90%) in less than 12 s. The detection and pose estimation algorithms evaluated using the LINEMOD database were similar to the state-of-the-art method, and were the most computationally efficient. MDPI 2018-07-24 /pmc/articles/PMC6111334/ /pubmed/30042372 http://dx.doi.org/10.3390/s18082408 Text en © 2018 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ivorra, Eugenio Ortega, Mario Catalán, José M. Ezquerro, Santiago Lledó, Luis Daniel Garcia-Aracil, Nicolás Alcañiz, Mariano Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms |
title | Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms |
title_full | Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms |
title_fullStr | Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms |
title_full_unstemmed | Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms |
title_short | Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms |
title_sort | intelligent multimodal framework for human assistive robotics based on computer vision algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111334/ https://www.ncbi.nlm.nih.gov/pubmed/30042372 http://dx.doi.org/10.3390/s18082408 |
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