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Walking Recognition in Mobile Devices
dc.contributor.author | Casado, Fernando E. | |
dc.contributor.author | Rodríguez García, Germán | |
dc.contributor.author | Iglesias Rodríguez, Roberto | |
dc.contributor.author | Regueiro, Carlos V. | |
dc.contributor.author | Barro, Senén | |
dc.contributor.author | Canedo-Rodriguez, Adrián | |
dc.date.accessioned | 2020-03-17T08:51:02Z | |
dc.date.available | 2020-03-17T08:51:02Z | |
dc.date.issued | 2020-02-21 | |
dc.identifier.citation | Casado, F.E.; Rodríguez, G.; Iglesias, R.; Regueiro, C.V.; Barro, S.; Canedo-Rodríguez, A. Walking Recognition in Mobile Devices. Sensors 2020, 20, 1189. https://doi.org/10.3390/s20041189 | es_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/2183/25175 | |
dc.description.abstract | [Abstract] 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. | es_ES |
dc.description.sponsorship | This research has received financial support from AEI/FEDER (European Union) grant number TIN2017-90135-R, as well as the Consellería de Cultura, Educación e Ordenación Universitaria of Galicia (accreditation 2016–2019, ED431G/01 and ED431G/08, reference competitive group ED431C2018/29, and grant ED431F2018/02), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Educación, Cultura y Deporte of Spain in the FPU 2017 program (FPU17/04154), and the Ministerio de Economía, Industria y Competitividad in the Industrial PhD 2014 program (DI-14-06920) | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/08 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C2018/29 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431F2018/02 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/FP7/04154 | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/TIN2017-90135-R/ES/APRENDIZAJE MAQUINA "GLOCAL" Y CONTINUO PARA UNA SOCIEDAD DE DISPOSITIVOS INTELIGENTES | |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DI-14-06920/ES/ | |
dc.relation | info:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/FPU17%2F04154/ES/ | |
dc.relation.uri | https://doi.org/10.3390/s20041189 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Walking recognition | es_ES |
dc.subject | Activity recognition | es_ES |
dc.subject | Smartphones | es_ES |
dc.subject | Inertial sensor fusion | es_ES |
dc.subject | Pattern classification | es_ES |
dc.subject | Time series classification | es_ES |
dc.title | Walking Recognition in Mobile Devices | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Sensors | es_ES |
UDC.volume | 20 | es_ES |
UDC.issue | 4 | es_ES |
UDC.startPage | 1189 | es_ES |
dc.identifier.doi | 10.3390/s20041189 |
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