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MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors
Human activity recognition (HAR) through sensors embedded in smartphones has allowed for the development of systems that are capable of detecting and monitoring human behavior. However, such systems have been affected by the high consumption of computational resources (e.g., memory and processing) n...
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/PMC6308833/ https://www.ncbi.nlm.nih.gov/pubmed/30544667 http://dx.doi.org/10.3390/s18124354 |
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author | Montero Quispe, Kevin G. Sousa Lima, Wesllen Macêdo Batista, Daniel Souto, Eduardo |
author_facet | Montero Quispe, Kevin G. Sousa Lima, Wesllen Macêdo Batista, Daniel Souto, Eduardo |
author_sort | Montero Quispe, Kevin G. |
collection | PubMed |
description | Human activity recognition (HAR) through sensors embedded in smartphones has allowed for the development of systems that are capable of detecting and monitoring human behavior. However, such systems have been affected by the high consumption of computational resources (e.g., memory and processing) needed to effectively recognize activities. In addition, existing HAR systems are mostly based on supervised classification techniques, in which the feature extraction process is done manually, and depends on the knowledge of a specialist. To overcome these limitations, this paper proposes a new method for recognizing human activities based on symbolic representation algorithms. The method, called “Multivariate Bag-Of-SFA-Symbols” (MBOSS), aims to increase the efficiency of HAR systems and maintain accuracy levels similar to those of conventional systems based on time and frequency domain features. The experiments conducted on three public datasets showed that MBOSS performed the best in terms of accuracy, processing time, and memory consumption. |
format | Online Article Text |
id | pubmed-6308833 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63088332019-01-04 MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors Montero Quispe, Kevin G. Sousa Lima, Wesllen Macêdo Batista, Daniel Souto, Eduardo Sensors (Basel) Article Human activity recognition (HAR) through sensors embedded in smartphones has allowed for the development of systems that are capable of detecting and monitoring human behavior. However, such systems have been affected by the high consumption of computational resources (e.g., memory and processing) needed to effectively recognize activities. In addition, existing HAR systems are mostly based on supervised classification techniques, in which the feature extraction process is done manually, and depends on the knowledge of a specialist. To overcome these limitations, this paper proposes a new method for recognizing human activities based on symbolic representation algorithms. The method, called “Multivariate Bag-Of-SFA-Symbols” (MBOSS), aims to increase the efficiency of HAR systems and maintain accuracy levels similar to those of conventional systems based on time and frequency domain features. The experiments conducted on three public datasets showed that MBOSS performed the best in terms of accuracy, processing time, and memory consumption. MDPI 2018-12-10 /pmc/articles/PMC6308833/ /pubmed/30544667 http://dx.doi.org/10.3390/s18124354 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 Montero Quispe, Kevin G. Sousa Lima, Wesllen Macêdo Batista, Daniel Souto, Eduardo MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors |
title | MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors |
title_full | MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors |
title_fullStr | MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors |
title_full_unstemmed | MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors |
title_short | MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors |
title_sort | mboss: a symbolic representation of human activity recognition using mobile sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308833/ https://www.ncbi.nlm.nih.gov/pubmed/30544667 http://dx.doi.org/10.3390/s18124354 |
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