Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1929
Full metadata record
DC FieldValueLanguage
dc.contributor299983es_ES
dc.contributor429892es_ES
dc.contributor326164es_ES
dc.contributor266942es_ES
dc.contributor.other0000-0002-7635-4687es_ES
dc.contributor.otherhttps://orcid.org/0000-0002-9498-6602-
dc.contributor.other0000-0002-9498-6602-
dc.contributor.otherhttps://orcid.org/0000-0001-6082-1546-
dc.coverage.spatialGlobales_ES
dc.creatorGalván Tejada, Carlos Eric-
dc.creatorLópez Monteagudo, Francisco Eneldo-
dc.creatorAlonso González, Omero-
dc.creatorGalván Tejada, Jorge Issac-
dc.creatorCelaya Padilla, José María-
dc.creatorGamboa Rosales, Hamurabi-
dc.creatorMagallanes Quintanar, Rafael-
dc.creatorZanella Calzada, Laura Alejandra-
dc.date.accessioned2020-05-21T19:13:36Z-
dc.date.available2020-05-21T19:13:36Z-
dc.date.issued2018-03-10-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.issn2220-9964es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1929-
dc.identifier.urihttps://doi.org/10.48779/f2ak-e441-
dc.description.abstractThe indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (eg, hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Several approaches have been proposed to tackle this problem. In this paper, we present the development of an indoor location system which relies on the human activity recognition approach, using sound as an information source to infer the indoor location based on the contextual information of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user. We evaluate the quality of the resulting model in terms of sensitivity and specificity for each location, and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc.) were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve an estimation that is 95% correct.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationhttps://www.mdpi.com/2220-9964/7/3/81es_ES
dc.relation.urigeneralPublices_ES
dc.sourceInternational Journal of Geo-Information Vol.7, No.3, pp. 1-16es_ES
dc.subject.classificationINGENIERIA Y TECNOLOGIA [7]es_ES
dc.subject.otherCADes_ES
dc.subject.otherindoor locationes_ES
dc.subject.otherhuman activity recognitiones_ES
dc.subject.othercontext informationes_ES
dc.subject.otherrandom forestes_ES
dc.subject.othermachine learning algorithmses_ES
dc.titleA generalized model for indoor location estimation using environmental sound from human activity recognitiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
Appears in Collections:*Documentos Académicos*-- M. en Ciencias del Proc. de la Info.

Files in This Item:
File Description SizeFormat 
ijgi-07-00081-v2.pdf2,15 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.