Comparing person-specific and independent models on subject-dependent and independent human activity recognition performance

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dc.contributor.authorScheurer, Sebastian
dc.contributor.authorTedesco, Salvatore
dc.contributor.authorO'Flynn, Brendan
dc.contributor.authorBrown, Kenneth N.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderSeventh Framework Programmeen
dc.contributor.funderEnterprise Irelanden
dc.date.accessioned2020-08-12T10:27:46Z
dc.date.available2020-08-12T10:27:46Z
dc.date.issued2020-06-29
dc.date.updated2020-08-12T10:15:51Z
dc.description.abstractThe distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independent performance, whether a model trained with data from many users achieves better subject-independent performance than one trained with data from a single person, and whether one trained with data from a single specific target user performs better for that user than one trained with data from many. To those ends, we compare four popular machine learning algorithms’ subject-dependent and subject-independent performances across eight datasets using three different personalisation–generalisation approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). We further consider three different ways to construct such an ensemble: unweighted, κ -weighted, and baseline-feature-weighted. Our analysis shows that PSMs outperform PIMs by 43.5% in terms of their subject-dependent performances, whereas PIMs outperform PSMs by 55.9% and κ -weighted EPSMs—the best-performing EPSM type—by 16.4% in terms of the subject-independent performance.en
dc.description.sponsorshipEuropean Commission (European-funded project SAFESENS under the ENIAC program); Enterprise Ireland (under grant number IR20140024)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid3647en
dc.identifier.citationScheurer, S., Tedesco, S., O'Flynn, B. and Brown, K. N. (2020) 'Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance', Sensors, 20(13), 3647 (27 pp). doi: 10.3390/s20133647en
dc.identifier.doi10.3390/s20133647en
dc.identifier.endpage27en
dc.identifier.issn1424-8220
dc.identifier.issued13en
dc.identifier.journaltitleSensorsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/10384
dc.identifier.volume20en
dc.language.isoenen
dc.publisherMDPIen
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.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/FP7::SP1::SP1-JTI/621272/EU/Sensor technologies enhanced safety and security of buildings and its occupants/SAFESENSen
dc.relation.urihttps://www.mdpi.com/1424-8220/20/13/3647
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectHuman activity recognitionen
dc.subjectMachine learningen
dc.subjectEnsemble methodsen
dc.subjectBoosting; baggingen
dc.subjectInertial sensorsen
dc.titleComparing person-specific and independent models on subject-dependent and independent human activity recognition performanceen
dc.typeArticle (peer-reviewed)en
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