STConvSleepNet: A Spatiotemporal Convolutional Network for Sleep Posture Detection

Page view(s)
7
Checked on Feb 06, 2025
STConvSleepNet: A Spatiotemporal Convolutional Network for Sleep Posture Detection
Title:
STConvSleepNet: A Spatiotemporal Convolutional Network for Sleep Posture Detection
Journal Title:
46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DOI:
Keywords:
Publication Date:
27 August 2024
Citation:
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
Description:
© 2024 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
979-8-3503-7149-9
Files uploaded:

File Size Format Action
embc-stconvsleepnet05.pdf 603.07 KB PDF Request a copy