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Low Energy Physical Activity Recognition System on Smartphones

An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little e...

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Autores principales: Morillo, Luis Miguel Soria, Gonzalez-Abril, Luis, Ramirez, Juan Antonio Ortega, de la Concepcion, Miguel Angel Alvarez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4435175/
https://www.ncbi.nlm.nih.gov/pubmed/25742171
http://dx.doi.org/10.3390/s150305163
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author Morillo, Luis Miguel Soria
Gonzalez-Abril, Luis
Ramirez, Juan Antonio Ortega
de la Concepcion, Miguel Angel Alvarez
author_facet Morillo, Luis Miguel Soria
Gonzalez-Abril, Luis
Ramirez, Juan Antonio Ortega
de la Concepcion, Miguel Angel Alvarez
author_sort Morillo, Luis Miguel Soria
collection PubMed
description An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the χ(2) distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy.
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spelling pubmed-44351752015-05-19 Low Energy Physical Activity Recognition System on Smartphones Morillo, Luis Miguel Soria Gonzalez-Abril, Luis Ramirez, Juan Antonio Ortega de la Concepcion, Miguel Angel Alvarez Sensors (Basel) Article An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the χ(2) distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy. MDPI 2015-03-03 /pmc/articles/PMC4435175/ /pubmed/25742171 http://dx.doi.org/10.3390/s150305163 Text en © 2015 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/4.0/).
spellingShingle Article
Morillo, Luis Miguel Soria
Gonzalez-Abril, Luis
Ramirez, Juan Antonio Ortega
de la Concepcion, Miguel Angel Alvarez
Low Energy Physical Activity Recognition System on Smartphones
title Low Energy Physical Activity Recognition System on Smartphones
title_full Low Energy Physical Activity Recognition System on Smartphones
title_fullStr Low Energy Physical Activity Recognition System on Smartphones
title_full_unstemmed Low Energy Physical Activity Recognition System on Smartphones
title_short Low Energy Physical Activity Recognition System on Smartphones
title_sort low energy physical activity recognition system on smartphones
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4435175/
https://www.ncbi.nlm.nih.gov/pubmed/25742171
http://dx.doi.org/10.3390/s150305163
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