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dc.contributor.authorCasagrande, Flavia Dias
dc.contributor.authorNedrejord, Oda Olsen
dc.contributor.authorLee, Wonho
dc.contributor.authorZouganeli, Evi
dc.date.accessioned2020-01-10T12:54:35Z
dc.date.accessioned2020-04-08T11:41:46Z
dc.date.available2020-01-10T12:54:35Z
dc.date.available2020-04-08T11:41:46Z
dc.date.issued2019-08-05
dc.identifier.citationCasagrande FDC, Nedrejord OO, Lee W, Zouganeli E P. Action Recognition in Real Homes using Low Resolution Depth Video Data . Computer-Based Medical Systems. 2019;2019-June:156-161en
dc.identifier.issn1063-7125
dc.identifier.issn2372-9198
dc.identifier.urihttps://hdl.handle.net/10642/8405
dc.description.abstractWe report work in progress from interdisciplinary research on Assisted Living Technology in smart homes for older adults with mild cognitive impairments or dementia. We present our field trial, the set-up for collecting and storing data from real homes, and preliminary results on action recognition using low resolution depth video cameras. The data have been collected from seven apartments with one resident each over a period of two weeks. We propose a pre-processing of the depth videos by applying an Infinite Response Filter (IIR) for extracting the movements in the frames prior to classification. In this work we classify four actions: TV interaction (turn it on/ off and switch over), standing up, sitting down, and no movement. Our first results indicate that using the IIR filter for movement information extraction improves accuracy and can be an efficient method for recognizing actions. Our current implementation uses a convolutional long short-term memory (ConvLSTM) neural network, and achieved an average peak accuracy of 86%.en
dc.description.sponsorshipFinanced by the Norwegian Research Council under the SAMANSVAR programme (247620/O70).en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartofseriesAnnual IEEE Symposium on Computer-Based Medical Systems;2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectDepth videosen
dc.subjectNeural networksen
dc.subjectSmart homesen
dc.subjectLow resolutionsen
dc.subjectAction recognitionen
dc.titleAction Recognition in Real Homes using Low Resolution Depth Video Dataen
dc.typeConference objecten
dc.date.updated2020-01-10T12:54:35Z
dc.description.versionacceptedVersionen
dc.identifier.doihttps://dx.doi.org/10.1109/CBMS.2019.00041
dc.identifier.cristin1749599
dc.source.journalIEEE International Symposium on Computer-Based Medical Systems


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