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

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dc.contributor.author Scheurer, Sebastian
dc.contributor.author Tedesco, Salvatore
dc.contributor.author O'Flynn, Brendan
dc.contributor.author Brown, Kenneth N.
dc.date.accessioned 2020-08-12T10:27:46Z
dc.date.available 2020-08-12T10:27:46Z
dc.date.issued 2020-06-29
dc.identifier.citation Scheurer, 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/s20133647 en
dc.identifier.volume 20 en
dc.identifier.issued 13 en
dc.identifier.startpage 1 en
dc.identifier.endpage 27 en
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10468/10384
dc.identifier.doi 10.3390/s20133647 en
dc.description.abstract The 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.sponsorship European Commission (European-funded project SAFESENS under the ENIAC program); Enterprise Ireland (under grant number IR20140024) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher MDPI en
dc.relation.uri https://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.uri http://creativecommons.org/licenses/by/4.0/ en
dc.subject Human activity recognition en
dc.subject Machine learning en
dc.subject Ensemble methods en
dc.subject Boosting; bagging en
dc.subject Inertial sensors en
dc.title Comparing person-specific and independent models on subject-dependent and independent human activity recognition performance en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Salvatore Tedesco, Tyndall Micronano Electronics, University College Cork, Cork, Ireland. +353-21-490-3000 Email: salvatore.tedesco@tyndall.ie en
dc.internal.availability Full text available en
dc.date.updated 2020-08-12T10:15:51Z
dc.description.version Published Version en
dc.internal.rssid 531035629
dc.internal.rssid 523817998
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder European Regional Development Fund en
dc.contributor.funder Seventh Framework Programme en
dc.contributor.funder Enterprise Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Sensors en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress salvatore.tedesco@tyndall.ie en
dc.internal.IRISemailaddress k.brown@cs.ucc.ie en
dc.internal.IRISemailaddress sebastian.scheurer@insight-centre.org en
dc.internal.IRISemailaddress brendan.oflynn@tyndall.ie en
dc.identifier.articleid 3647 en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/ en
dc.relation.project info:eu-repo/grantAgreement/EC/FP7::SP1::SP1-JTI/621272/EU/Sensor technologies enhanced safety and security of buildings and its occupants/SAFESENS en


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© 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/). Except where otherwise noted, this item's license is described as © 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/).
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