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Article

A Blanket Accommodative Sleep Posture Classification System Using an Infrared Depth Camera: A Deep Learning Approach with Synthetic Augmentation of Blanket Conditions

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Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong, China
2
Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong, China
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Authors to whom correspondence should be addressed.
Academic Editors: Alexandra Psarrou, Jose Garcia Rodriguez, Sergio Orts-Escolano, Alberto Garcia-Garcia and Chen Chen
Sensors 2021, 21(16), 5553; https://doi.org/10.3390/s21165553
Received: 18 July 2021 / Revised: 14 August 2021 / Accepted: 17 August 2021 / Published: 18 August 2021
(This article belongs to the Special Issue Deep Learning Image Recognition Systems)
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%. View Full-Text
Keywords: sleep posture recognition; convolutional neural network; sleep disorder; sleep behavior; sleep monitoring; sleep surveillance sleep posture recognition; convolutional neural network; sleep disorder; sleep behavior; sleep monitoring; sleep surveillance
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MDPI and ACS Style

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 Blanket Accommodative Sleep Posture Classification System Using an Infrared Depth Camera: A Deep Learning Approach with Synthetic Augmentation of Blanket Conditions. Sensors 2021, 21, 5553. https://doi.org/10.3390/s21165553

AMA Style

Tam AY-C, So BP-H, Chan TT-C, Cheung AK-Y, Wong DW-C, Cheung JC-W. A Blanket Accommodative Sleep Posture Classification System Using an Infrared Depth Camera: A Deep Learning Approach with Synthetic Augmentation of Blanket Conditions. Sensors. 2021; 21(16):5553. https://doi.org/10.3390/s21165553

Chicago/Turabian Style

Tam, Andy Yiu-Chau, Bryan Pak-Hei So, Tim Tin-Chun Chan, Alyssa Ka-Yan Cheung, Duo Wai-Chi Wong, and James Chung-Wai Cheung. 2021. "A Blanket Accommodative Sleep Posture Classification System Using an Infrared Depth Camera: A Deep Learning Approach with Synthetic Augmentation of Blanket Conditions" Sensors 21, no. 16: 5553. https://doi.org/10.3390/s21165553

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