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Walking Recognition in Mobile Devices

Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localiza...

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Autores principales: Casado, Fernando E., Rodríguez, Germán, Iglesias, Roberto, Regueiro, Carlos V., Barro, Senén, Canedo-Rodríguez, Adrián
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071017/
https://www.ncbi.nlm.nih.gov/pubmed/32098082
http://dx.doi.org/10.3390/s20041189
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author Casado, Fernando E.
Rodríguez, Germán
Iglesias, Roberto
Regueiro, Carlos V.
Barro, Senén
Canedo-Rodríguez, Adrián
author_facet Casado, Fernando E.
Rodríguez, Germán
Iglesias, Roberto
Regueiro, Carlos V.
Barro, Senén
Canedo-Rodríguez, Adrián
author_sort Casado, Fernando E.
collection PubMed
description Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposals.
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spelling pubmed-70710172020-03-19 Walking Recognition in Mobile Devices Casado, Fernando E. Rodríguez, Germán Iglesias, Roberto Regueiro, Carlos V. Barro, Senén Canedo-Rodríguez, Adrián Sensors (Basel) Article Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposals. MDPI 2020-02-21 /pmc/articles/PMC7071017/ /pubmed/32098082 http://dx.doi.org/10.3390/s20041189 Text en © 2020 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
Casado, Fernando E.
Rodríguez, Germán
Iglesias, Roberto
Regueiro, Carlos V.
Barro, Senén
Canedo-Rodríguez, Adrián
Walking Recognition in Mobile Devices
title Walking Recognition in Mobile Devices
title_full Walking Recognition in Mobile Devices
title_fullStr Walking Recognition in Mobile Devices
title_full_unstemmed Walking Recognition in Mobile Devices
title_short Walking Recognition in Mobile Devices
title_sort walking recognition in mobile devices
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071017/
https://www.ncbi.nlm.nih.gov/pubmed/32098082
http://dx.doi.org/10.3390/s20041189
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