Subject-dependent and -independent human activity recognition with person-specific and -independent models

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dc.contributor.authorScheurer, Sebastian
dc.contributor.authorTedesco, Salvatore
dc.contributor.authorBrown, Kenneth N.
dc.contributor.authorO'Flynn, Brendan
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2020-02-13T10:36:26Z
dc.date.available2020-02-13T10:36:26Z
dc.date.issued2019-09-16
dc.date.updated2020-02-10T12:29:02Z
dc.description.abstractThe distinction between subject-dependent and subject-independent performance is ubiquitous in the Human Activity Recognition (HAR) literature. We test the hypotheses that HAR models achieve better subject-dependent performance than subject-independent performance, that a model trained with many users will achieve better subject-independent performance than one trained with a single user, and that one trained with a single user performs better for that user than one trained with this and other users by comparing four algorithms' subject-dependent and -independent performance across eight data sets using three different approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). Our analysis shows that PSMs outperform PIMs by 3.5% for known users, PIMs outperform PSMs by 13.9% and ensembles of PSMs by a not significant 2.1% for unknown users, and that the performance for known users is 20.5% to 48% better than for unknown users.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationScheurer, S., Tedesco, S., Brown, K. N. and O'Flynn, B. (2019) Subject-dependent and -independent human activity recognition with person-specific and -independent models Proceedings of the 6th international Workshop on Sensor-based Activity Recognition and Interaction Rostock, Germany, 16-19 September. doi: 10.1145/3361684.3361689en
dc.identifier.doi10.1145/3361684.3361689en
dc.identifier.endpage7en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/9643
dc.language.isoenen
dc.publisherAssociation for Computing Machinery, ACMen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://doi.org/10.1145/3361684.3361689
dc.rights© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in HTTF 2019: Proceedings of the Halfway to the Future Symposium 2019, https://doi.org/10.1145/3363384.3363485en
dc.subjectBaggingen
dc.subjectBoostingen
dc.subjectInertial sensorsen
dc.subjectMachine learningen
dc.subjectEnsemble methodsen
dc.subjectHuman activity recognitionen
dc.titleSubject-dependent and -independent human activity recognition with person-specific and -independent modelsen
dc.typeConference itemen
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