SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB

• Published in: IEEE journal of biomedical and health informatics 2021-04, Vol.25 (4), p.1305-1314
• Main Authors: Piriyajitakonkij, Maytus, Warin, Patchanon, Lakhan, Payongkit, Leelaarporn, Pitshaporn, Kumchaiseemak, Nakorn, Suwajanakorn, Supasorn, Pianpanit, Theerasarn, Niparnan, Nattee, Mukhopadhyay, Subhas Chandra, Wilaiprasitporn, Theerawit
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Biomedical monitoring; Classification; contactless sensing; Convolution; Data augmentation; Deep learning; Doppler radar; Heart rate; Human activity recognition; Learning; Monitoring; Moving object recognition; Neural networks; Posture; Sleep; Sleep disorders; Sleep monitoring; sleep posture; Time series; Ultra wideband radar; Ultrawideband radar; UWB radar
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2020.3025900

Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of an off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. The data recorded from 38 volunteers displayed that SPN with a mean accuracy of 73.7 \pm 0.8 \% significantly outperformed the mean accuracy of 59.9 \pm 0.7 \% obtained from deep convolution neural network (DCNN) in recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications.