Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91981
PIRA download icon_1.1View/Download Full Text
Title: A blanket accommodative sleep posture classification system using an infrared depth camera : a deep learning approach with synthetic augmentation of blanket conditions
Authors: Tam, AYC 
So, BPH 
Chan, TTC 
Cheung, AKY 
Wong, DWC 
Cheung, JCW 
Issue Date: Aug-2021
Source: Sensors, Aug. 2021, v. 21, no. 16, 5553
Abstract: Surveillance 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%.
Keywords: Convolutional neural network
Sleep behavior
Sleep disorder
Sleep monitoring
Sleep posture recognition
Sleep surveillance
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Sensors 
EISSN: 1424-8220
DOI: 10.3390/s21165553
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/).
The 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/s21165553
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
sensors-21-05553.pdf3.23 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

3
Citations as of Aug 7, 2022

Downloads

1
Citations as of Aug 7, 2022

SCOPUSTM   
Citations

2
Citations as of Aug 4, 2022

WEB OF SCIENCETM
Citations

2
Citations as of Aug 4, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.