Device-Free People Counting in IoT Environments: New Insights, Results, and Open Challenges

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dc.contributor.authorSobron, Iker
dc.contributor.authorDel Ser, Javier
dc.contributor.authorEizmendi, Inaki
dc.contributor.authorVelez, Manuel
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T12:09:01Z
dc.date.available2024-07-24T12:09:01Z
dc.date.issued2018-12
dc.descriptionPublisher Copyright: © 2014 IEEE.
dc.description.abstractIn the last years multiple Internet of Things (IoT) solutions have been developed to detect, track, count, and identify human activity from people that do not carry any device nor participate actively in the detection process. When WiFi radio receivers are employed as sensors for device-free human activity recognition, channel quality measurements are preprocessed in order to extract predictive features toward performing the desired activity recognition via machine learning (ML) models. Despite the variety of predictors in the literature, there is no universally outperforming set of features for all scenarios and applications. However, certain feature combinations could achieve a better average detection performance compared to the use of a thorough feature portfolio. Such predictors are often obtained by feature engineering and selection techniques applied before the learning process. This manuscript elaborates on the feature engineering and selection methodology for counting device-free people by solely resorting to the fluctuation and variation of WiFi signals exchanged by IoT devices. We comprehensively review the feature engineering and ML models employed in the literature from a critical perspective, identifying trends, research niches, and open challenges. Furthermore, we present and provide the community with a new open database with WiFi measurements in several indoor environments (i.e., rooms, corridors, and stairs) where up to five people can be detected. This dataset is used to exhaustively assess the performance of different ML models with and without feature selection, from which insightful conclusions are drawn regarding the predictive potential of different predictors across scenarios of diverse characteristics.en
dc.description.sponsorshipManuscript received November 29, 2017; revised January 24, 2018; accepted February 8, 2018. Date of publication February 16, 2018; date of current version January 16, 2019. This work was supported in part by the Spanish Ministry of Economy and Competitiveness (TEC2015-66153-P MINECO/FEDER, EU) under Project 5GnewBROS and in part by the Basque Government (IT683-13 and the EMAITEK Program). (Corresponding author: Iker Sobron.) I. Sobron, I. Eizmendi, and M. Vélez are with the Department of Communications Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain (e-mail: iker.sobron@ehu.eus; inaki.eizmendi@ehu.eus; manuel.velez@ehu.eus).
dc.description.statusPeer reviewed
dc.format.extent13
dc.identifier.citationSobron , I , Del Ser , J , Eizmendi , I & Velez , M 2018 , ' Device-Free People Counting in IoT Environments : New Insights, Results, and Open Challenges ' , IEEE Internet of Things Journal , vol. 5 , no. 6 , 8293759 , pp. 4396-4408 . https://doi.org/10.1109/JIOT.2018.2806990
dc.identifier.doi10.1109/JIOT.2018.2806990
dc.identifier.issn2327-4662
dc.identifier.urihttps://hdl.handle.net/11556/3931
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85042201558&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Internet of Things Journal
dc.relation.projectIDMINECO/FEDER
dc.relation.projectIDSpanish Ministry of Economy and Competitiveness, TEC2015-66153-P
dc.relation.projectIDEuropean Commission, EC, 5GnewBROS
dc.relation.projectIDEusko Jaurlaritza, IT683-13
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsDevice-free people counting
dc.subject.keywordsFeature selection (FS)
dc.subject.keywordsInternet of Things (IoT) architectures
dc.subject.keywordsMachine learning (ML)
dc.subject.keywordsSignal Processing
dc.subject.keywordsInformation Systems
dc.subject.keywordsHardware and Architecture
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsComputer Networks and Communications
dc.titleDevice-Free People Counting in IoT Environments: New Insights, Results, and Open Challengesen
dc.typejournal article
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