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dc.contributor.authorWijekoon, Anjana
dc.contributor.authorWiratunga, Nirmalie
dc.contributor.authorSani, Sadiq
dc.contributor.authorMassie, Stewart
dc.contributor.authorCooper, Kay
dc.date.accessioned2018-06-05T10:02:22Z
dc.date.available2018-06-05T10:02:22Z
dc.date.issued2018-10-09
dc.identifier.citationWIJEKOON, A., WIRATUNGA, N., SANI, S., MASSIE, S. and COOPER, K. 2018. Improving kNN for human activity recognition with privileged learning using translation models. In Cox, M.T., Funk, P. and Begum, S. (eds.) Lecture notes in computer science, 11156. Case-based reasoning research and development; proceedings of the 26th International conference on case-based reasoning (ICCBR-18), 9-12 July 2018, Stockholm, Sweden. Cham: Springer [online], pages 448-463. Available from: https://doi.org/10.1007/978-3-030-01081-2_30en
dc.identifier.isbn9783030010805
dc.identifier.isbn9783030010812
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/10059/2943
dc.description.abstractMultiple sensor modalities provide more accurate Human Activity Recognition (HAR) compared to using a single modality, yet the latter is preferred by consumers as it is more convenient and less intrusive. This presents a challenge to researchers, as a single modality is likely to pick up movement that is both relevant as well as extraneous to the human activity being tracked and lead to poorer performance. The goal of an optimal HAR solution is therefore to utilise the fewest sensors at deployment, while maintaining performance levels achievable using all available sensors. To this end, we introduce two translation approaches, capable of generating missing modalities from available modalities. These can be used to generate missing or 'privileged' modalities at deployment to augment case representations and improve HAR.We evaluate the presented translators with k-NN classifiers on two HAR datasets and achieve up-to 5% performance improvements using representations augmented with privileged modalities. This suggests that non-intrusive modalities suited for deployment benefit from translation models that generates privileged modalities.en
dc.language.isoenen
dc.publisherSpringeren
dc.rightshttps://creativecommons.org/licenses/by-nc/4.0en
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectHuman activity recognitionen
dc.subjectMachine learningen
dc.subjectCase representationen
dc.subjectPrivileged learningen
dc.titleImproving kNN for human activity recognition with privileged learning using translation models.en
dc.typeConference publicationen
dc.publisher.urihttps://doi.org/10.1007/978-3-030-01081-2_30
dcterms.dateAccepted2018-05-21
dcterms.publicationdate2018-11-08
refterms.accessExceptionNAen
refterms.dateDeposit2018-06-05
refterms.dateEmbargoEnd2019-10-09
refterms.dateFCA2019-10-09
refterms.dateFCD2018-06-05
refterms.dateFreeToDownload2019-10-09
refterms.dateFreeToRead2019-10-09
refterms.dateToSearch2019-10-09
refterms.depositExceptionNAen
refterms.panelBen
refterms.technicalExceptionNAen
refterms.versionAMen
rioxxterms.publicationdate2018-10-09
rioxxterms.typeConference Paper/Proceeding/Abstracten
rioxxterms.versionAM


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