SleepPoseNet: multi-view learning for sleep postural transition recognition using UWB

Maytus Piriyajitakonkij, Patchanon Warin, Payongkit Lakhan, Pitshaporn Leelaarporn, Nakorn Kumchaiseemak, Supasorn Suwajanakorn, Theerasarn Pianpanit, Nattee Niparnan, Subhas Chandra Mukhopadhyay, Theerawit Wilaiprasitporn*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

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±0.8% significantly outperformed the mean accuracy of 59.9±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.
Original languageEnglish
Pages (from-to)1305-1314
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume25
Issue number4
DOIs
Publication statusPublished - Apr 2021

Fingerprint Dive into the research topics of 'SleepPoseNet: multi-view learning for sleep postural transition recognition using UWB'. Together they form a unique fingerprint.

Cite this