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Using multivariate outliers from smartphone sensor data to detect physical barriers while walking in urban areas

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)es
dc.contributor.authorRuiz Blázquez, Ramona
dc.contributor.authorMuñoz Organero, Mario
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2021-02-24T12:48:45Z
dc.date.available2021-02-24T12:48:45Z
dc.date.issued2020-10-22
dc.description.abstractNowadays, our mobile devices have become smart computing platforms, incorporating a wide number of embedded sensors such as accelerometers, gyroscopes, barometers, GPS receivers, and magnetometers. Smartphones are valuable devices for gathering user-related data and transforming it into value-added information for the user. In this study, a novel mechanism to process sensor data from mobile devices in order to detect the type of area the user is crossing while walking in an urban setting is presented. The method is based on combining outlier data analysis and classification techniques from data collected by several pedestrians while traversing an urban environment. A theoretical framework, composed of methods for detecting multivariate outliers combined with supervised classification techniques, has been proposed in order to identify different situations and physical barriers while walking. Each type of element to be detected is characterized by using a feature vector computed based on the outliers detected. Finally, a radial SVM is used for the classification task. The classifier is trained in a supervised way with data from 20 different segments containing several physical barriers and used later to assign a class to new un-labelled data. The results obtained with this approach are very promising with an average accuracy around 95% when detecting different types of physical barriers.en
dc.description.sponsorshipThe research leading to these results has received funding from the “ANALYTICS USING SENSORDATA FOR FLATCITY” project TIN2016-77158-C4-1-R (MINECO/ERDF, EU) funded by the Spanish AgenciaEstatal de Investigación (AEI) and the European Regional Development Fund (ERDF). Furthermore, the firstauthor is supported by the MINECO Grant nº: BES-2014-070462en
dc.identifier.bibliographicCitationRuiz Blázquez, R.; Muñoz-Organero, M. Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas. Technologies 2020, 8, 58
dc.identifier.doihttps://doi.org/10.3390/technologies8040058
dc.identifier.issn2227-7080
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue58
dc.identifier.publicationlastpage13
dc.identifier.publicationtitleTechnologies
dc.identifier.publicationvolume8
dc.identifier.urihttps://hdl.handle.net/10016/32012
dc.identifier.uxxiAR/0000026424
dc.language.isoeng
dc.publisherMDPI
dc.relation.projectIDGobierno de España. TIN2016-77158-C4-1-R
dc.relation.projectIDGobierno de España. BES-2014-070462es
dc.rights©2020 by the authors
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subject.otherMultivariate outliersen
dc.subject.otherMachine learningen
dc.subject.otherSVMen
dc.subject.otherMobile sensor dataen
dc.titleUsing multivariate outliers from smartphone sensor data to detect physical barriers while walking in urban areasen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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