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...
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/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. |
format | Online Article Text |
id | pubmed-5948724 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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