Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot

Ambient assisted living (AAL) environments are currently a key focus of interest as an option to assist and monitor disabled and elderly people. These systems can improve their quality of life and personal autonomy by detecting events such as entering potentially dangerous areas, potential fall even...

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Main Authors: Francisco Gomez-Donoso, Félix Escalona, Francisco Miguel Rivas, Jose Maria Cañas, Miguel Cazorla
Format: Article
Language:English
Published: Hindawi Limited 2019-01-01
Series:Computational Intelligence and Neuroscience
Online Access:http://dx.doi.org/10.1155/2019/9412384
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spelling doaj-10d6855c8d0947dd97649af4bb9a57162020-11-24T21:52:57ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732019-01-01201910.1155/2019/94123849412384Enhancing the Ambient Assisted Living Capabilities with a Mobile RobotFrancisco Gomez-Donoso0Félix Escalona1Francisco Miguel Rivas2Jose Maria Cañas3Miguel Cazorla4Institute for Computer Research, University of Alicante, P.O. Box 99, 03080 Alicante, SpainInstitute for Computer Research, University of Alicante, P.O. Box 99, 03080 Alicante, SpainRoboticsLab-URJC, Universidad Rey Juan Carlos, Madrid, SpainRoboticsLab-URJC, Universidad Rey Juan Carlos, Madrid, SpainInstitute for Computer Research, University of Alicante, P.O. Box 99, 03080 Alicante, SpainAmbient assisted living (AAL) environments are currently a key focus of interest as an option to assist and monitor disabled and elderly people. These systems can improve their quality of life and personal autonomy by detecting events such as entering potentially dangerous areas, potential fall events, or extended stays in the same place. Nonetheless, there are areas that remain outside the scope of AAL systems due to the placement of cameras. There also exist sources of danger in the scope of the camera that the AAL system cannot detect. These sources of danger are relatively small in size, occluded, or nonstatic. To solve this problem, we propose the inclusion of a robot which maps such uncovered areas looking for new potentially dangerous areas that go unnoticed by the AAL. The robot then sends this information to the AAL system in order to improve its performance. Experimentation in real-life scenarios successfully validates our approach.http://dx.doi.org/10.1155/2019/9412384
collection DOAJ
language English
format Article
sources DOAJ
author Francisco Gomez-Donoso
Félix Escalona
Francisco Miguel Rivas
Jose Maria Cañas
Miguel Cazorla
spellingShingle Francisco Gomez-Donoso
Félix Escalona
Francisco Miguel Rivas
Jose Maria Cañas
Miguel Cazorla
Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot
Computational Intelligence and Neuroscience
author_facet Francisco Gomez-Donoso
Félix Escalona
Francisco Miguel Rivas
Jose Maria Cañas
Miguel Cazorla
author_sort Francisco Gomez-Donoso
title Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot
title_short Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot
title_full Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot
title_fullStr Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot
title_full_unstemmed Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot
title_sort enhancing the ambient assisted living capabilities with a mobile robot
publisher Hindawi Limited
series Computational Intelligence and Neuroscience
issn 1687-5265
1687-5273
publishDate 2019-01-01
description Ambient assisted living (AAL) environments are currently a key focus of interest as an option to assist and monitor disabled and elderly people. These systems can improve their quality of life and personal autonomy by detecting events such as entering potentially dangerous areas, potential fall events, or extended stays in the same place. Nonetheless, there are areas that remain outside the scope of AAL systems due to the placement of cameras. There also exist sources of danger in the scope of the camera that the AAL system cannot detect. These sources of danger are relatively small in size, occluded, or nonstatic. To solve this problem, we propose the inclusion of a robot which maps such uncovered areas looking for new potentially dangerous areas that go unnoticed by the AAL. The robot then sends this information to the AAL system in order to improve its performance. Experimentation in real-life scenarios successfully validates our approach.
url http://dx.doi.org/10.1155/2019/9412384
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