Dikun H., Weidong G., Keng K.A., Mengjiao H. STConvSleepNet: A Spatiotemporal Convolutional Network for Sleep Posture Detection; Proceedings of the 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Orlando, FL, USA. 15–19 July 202
Abstract:
Sleep posture, intricately connected to sleep health,
has emerged as a crucial focus in sleep medicine. Studies have
associated the supine posture with increased frequency and
severity of obstructive sleep apnea (OSA), while lateral postures
may mitigate these effects. For bedridden patients, regular
posture adjustments are essential to prevent ulcers and bedsores,
highlighting the need for precise sleep posture detection. In this
work, we propose STConvSleepNet, a novel method for
detecting sleep posture using piezoelectric sensor pressure data.
It employs two shallow CNN2D networks to discriminate spatial
features and two CNN1D networks to discriminate temporal
features, with each network processing either the heart rate or
the respiratory rate. These networks are trained to detect sleep
postures from spatial features of the pressure distribution, and
temporal features of heart rate and cardiopulmonary activities
variability. We collected data from 22 participants with 300-450
samples each, for a total of 8583 samples using a 32-sensor array.
We performed 5-fold cross-validation on the data using the
proposed method. The results showed that the proposed
STConvSleepNet yielded 91.11% recall, 92.89% precision, and
94.93% accuracy. This is comparable to the state-of-the-art
method that needs a significantly increased number of sensors to
achieve slightly increased accuracy of 96.90%. Hence these
results showed promise of using the proposed STConvSleepNet
for cost-effective home sleep monitoring using portable devices.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Project of Guangdong Province - N/A
Grant Reference no. : 2022B1515130009
This research / project is supported by the Special subject on Agriculture and Social Development - N/A
Grant Reference no. : 2023B03J0172