TY - JOUR
T1 - SleepPoseNet
T2 - multi-view learning for sleep postural transition recognition using UWB
AU - Piriyajitakonkij, Maytus
AU - Warin, Patchanon
AU - Lakhan, Payongkit
AU - Leelaarporn, Pitshaporn
AU - Kumchaiseemak, Nakorn
AU - Suwajanakorn, Supasorn
AU - Pianpanit, Theerasarn
AU - Niparnan, Nattee
AU - Mukhopadhyay, Subhas Chandra
AU - Wilaiprasitporn, Theerawit
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85104047148&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2020.3025900
DO - 10.1109/JBHI.2020.3025900
M3 - Article
C2 - 32960771
AN - SCOPUS:85104047148
VL - 25
SP - 1305
EP - 1314
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 4
ER -