Where are you?: Human activity recognition with smartphone sensor data

dc.authorscopusid40661216400
dc.authorscopusid57219241439
dc.authorscopusid57219241855
dc.authorscopusid57207578448
dc.authorscopusid57219243763
dc.authorscopusid57219240961
dc.authorwosidGAJ-2541-2022
dc.authorwosidCJT-8692-2022
dc.authorwosidEKL-6235-2022
dc.authorwosidKKF-8130-2024
dc.authorwosidCZU-9341-2022
dc.authorwosidWOS:000842375700069
dc.contributor.authorDogan, G.
dc.contributor.authorÇay, İremnaz
dc.contributor.authorErtaş, Sinem Sena
dc.contributor.authorKeskin, S.R.
dc.contributor.authorAlotaibi, N.
dc.contributor.authorSahin, E.
dc.date.accessioned2020-12-20T06:49:56Z
dc.date.available2020-12-20T06:49:56Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesien_US
dc.descriptionACM SIGCHI;ACM SIGMOBILEen_US
dc.description2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2020 -- 12 September 2020 through 17 September 2020 -- -- 162964en_US
dc.description.abstractThis paper describes our submission as Team-Petrichor to the competition that was organized by the SHL recognition challenge dataset authors. We compared multiple machine learning approach for classifying eight different activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). The first step was feature engineering, a wide set of statistical domain features were computed and their quality was evaluated. Finally, the appropriate machine learning model was chosen. The recognition result for the testing dataset will be presented in the summary paper of the SHL recognition challenge. © 2020 ACM.en_US
dc.identifier.doi10.1145/3410530.3414354
dc.identifier.endpage304en_US
dc.identifier.isbn9781450380768
dc.identifier.scopus2-s2.0-85091839543
dc.identifier.scopusqualityN/A
dc.identifier.startpage301en_US
dc.identifier.urihttps://doi.org/10.1145/3410530.3414354
dc.identifier.urihttps://hdl.handle.net/20.500.12436/1869
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÇay, İremnaz
dc.institutionauthorErtaş, Sinem Sena
dc.language.isoen
dc.publisherAssociation for Computing Machineryen_US
dc.relation.ispartofUbiComp/ISWC 2020 Adjunct - Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computersen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectactivity recognitionen_US
dc.subjectlocomotion classificationen_US
dc.subjectmachine learningen_US
dc.subjecttransportation mode predictionen_US
dc.titleWhere are you?: Human activity recognition with smartphone sensor dataen_US
dc.typeConference Object
dspace.entity.typePublication

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