Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91981
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dc.contributorDepartment of Biomedical Engineering-
dc.contributorResearch Institute for Smart Ageing-
dc.creatorTam, AYC-
dc.creatorSo, BPH-
dc.creatorChan, TTC-
dc.creatorCheung, AKY-
dc.creatorWong, DWC-
dc.creatorCheung, JCW-
dc.date.accessioned2022-02-07T07:04:45Z-
dc.date.available2022-02-07T07:04:45Z-
dc.identifier.urihttp://hdl.handle.net/10397/91981-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Tam, A.Y.-C.; So, B.P.-H.;Chan, T.T.-C.; Cheung, A.K.-Y.; Wong,D.W.-C.; Cheung, J.C.-W. A BlanketAccommodative Sleep PostureClassification System Using anInfrared Depth Camera: A DeepLearning Approach with SynthetiAugmentation of Blanket Conditions.Sensors 2021, 21, 5553 is available at https://doi.org/10.3390/s21165553en_US
dc.subjectConvolutional neural networken_US
dc.subjectSleep behavioren_US
dc.subjectSleep disorderen_US
dc.subjectSleep monitoringen_US
dc.subjectSleep posture recognitionen_US
dc.subjectSleep surveillanceen_US
dc.titleA blanket accommodative sleep posture classification system using an infrared depth camera : a deep learning approach with synthetic augmentation of blanket conditionsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume21-
dc.identifier.issue16-
dc.identifier.doi10.3390/s21165553-
dcterms.abstractSurveillance of sleeping posture is essential for bed-ridden patients or individuals at-risk of falling out of bed. Existing sleep posture monitoring and classification systems may not be able to accommodate the covering of a blanket, which represents a barrier to conducting pragmatic studies. The objective of this study was to develop an unobtrusive sleep posture classification that could accommodate the use of a blanket. The system uses an infrared depth camera for data acquisition and a convolutional neural network to classify sleeping postures. We recruited 66 participants (40 men and 26 women) to perform seven major sleeping postures (supine, prone (head left and right), log (left and right) and fetal (left and right)) under four blanket conditions (thick, medium, thin, and no blanket). Data augmentation was conducted by affine transformation and data fusion, generating additional blanket conditions with the original dataset. Coarse-grained (four-posture) and fine-grained (seven-posture) classifiers were trained using two fully connected network layers. For the coarse classification, the log and fetal postures were merged into a side-lying class and the prone class (head left and right) was pooled. The results show a drop of overall F1-score by 8.2% when switching to the fine-grained classifier. In addition, compared to no blanket, a thick blanket reduced the overall F1-scores by 3.5% and 8.9% for the coarse-and fine-grained classifiers, respectively; meanwhile, the lowest performance was seen in classifying the log (right) posture under a thick blanket, with an F1-score of 72.0%. In conclusion, we developed a system that can classify seven types of common sleeping postures under blankets and achieved an F1-score of 88.9%.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSensors, Aug. 2021, v. 21, no. 16, 5553-
dcterms.isPartOfSensors-
dcterms.issued2021-08-
dc.identifier.scopus2-s2.0-85112703409-
dc.identifier.eissn1424-8220-
dc.identifier.artn5553-
dc.description.validate202202 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceNot mentionen_US
dc.description.pubStatusPublisheden_US
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