Using Ontologies for the Online Recognition of Activities of Daily Living†

The recognition of activities of daily living is an important research area of interest in recent years. The process of activity recognition aims to recognize the actions of one or more people in a smart environment, in which a set of sensors has been deployed. Usually, all the events produced durin...

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Autores principales: Salguero, Alberto G., Espinilla, Macarena, Delatorre, Pablo, Medina, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948724/
https://www.ncbi.nlm.nih.gov/pubmed/29662011
http://dx.doi.org/10.3390/s18041202
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author Salguero, Alberto G.
Espinilla, Macarena
Delatorre, Pablo
Medina, Javier
author_facet Salguero, Alberto G.
Espinilla, Macarena
Delatorre, Pablo
Medina, Javier
author_sort Salguero, Alberto G.
collection PubMed
description The recognition of activities of daily living is an important research area of interest in recent years. The process of activity recognition aims to recognize the actions of one or more people in a smart environment, in which a set of sensors has been deployed. Usually, all the events produced during each activity are taken into account to develop the classification models. However, the instant in which an activity started is unknown in a real environment. Therefore, only the most recent events are usually used. In this paper, we use statistics to determine the most appropriate length of that interval for each type of activity. In addition, we use ontologies to automatically generate features that serve as the input for the supervised learning algorithms that produce the classification model. The features are formed by combining the entities in the ontology, such as concepts and properties. The results obtained show a significant increase in the accuracy of the classification models generated with respect to the classical approach, in which only the state of the sensors is taken into account. Moreover, the results obtained in a simulation of a real environment under an event-based segmentation also show an improvement in most activities.
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spelling pubmed-59487242018-05-17 Using Ontologies for the Online Recognition of Activities of Daily Living† Salguero, Alberto G. Espinilla, Macarena Delatorre, Pablo Medina, Javier Sensors (Basel) Article The recognition of activities of daily living is an important research area of interest in recent years. The process of activity recognition aims to recognize the actions of one or more people in a smart environment, in which a set of sensors has been deployed. Usually, all the events produced during each activity are taken into account to develop the classification models. However, the instant in which an activity started is unknown in a real environment. Therefore, only the most recent events are usually used. In this paper, we use statistics to determine the most appropriate length of that interval for each type of activity. In addition, we use ontologies to automatically generate features that serve as the input for the supervised learning algorithms that produce the classification model. The features are formed by combining the entities in the ontology, such as concepts and properties. The results obtained show a significant increase in the accuracy of the classification models generated with respect to the classical approach, in which only the state of the sensors is taken into account. Moreover, the results obtained in a simulation of a real environment under an event-based segmentation also show an improvement in most activities. MDPI 2018-04-14 /pmc/articles/PMC5948724/ /pubmed/29662011 http://dx.doi.org/10.3390/s18041202 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
Salguero, Alberto G.
Espinilla, Macarena
Delatorre, Pablo
Medina, Javier
Using Ontologies for the Online Recognition of Activities of Daily Living†
title Using Ontologies for the Online Recognition of Activities of Daily Living†
title_full Using Ontologies for the Online Recognition of Activities of Daily Living†
title_fullStr Using Ontologies for the Online Recognition of Activities of Daily Living†
title_full_unstemmed Using Ontologies for the Online Recognition of Activities of Daily Living†
title_short Using Ontologies for the Online Recognition of Activities of Daily Living†
title_sort using ontologies for the online recognition of activities of daily living†
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948724/
https://www.ncbi.nlm.nih.gov/pubmed/29662011
http://dx.doi.org/10.3390/s18041202
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