  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.
