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

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dc.contributor.author Ruiz Blázquez, Ramona
dc.contributor.author Muñoz Organero, Mario
dc.date.accessioned 2021-02-24T12:48:45Z
dc.date.available 2021-02-24T12:48:45Z
dc.date.issued 2020-10-22
dc.identifier.bibliographicCitation Ruiz 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.issn 2227-7080
dc.identifier.uri http://hdl.handle.net/10016/32012
dc.description.abstract Nowadays, 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.
dc.description.sponsorship The 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-2014-070462
dc.language.iso eng
dc.publisher MDPI
dc.rights ©2020 by the authors
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Multivariate outliers
dc.subject.other Machine learning
dc.subject.other SVM
dc.subject.other Mobile sensor data
dc.title Using multivariate outliers from smartphone sensor data to detect physical barriers while walking in urban areas
dc.type article
dc.identifier.doi https://doi.org/10.3390/technologies8040058
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2016-77158-C4-1-R
dc.relation.projectID Gobierno de España. BES-2014-070462
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 58
dc.identifier.publicationlastpage 13
dc.identifier.publicationtitle Technologies
dc.identifier.publicationvolume 8
dc.identifier.uxxi AR/0000026424
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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