STConvSleepNet: A Spatiotemporal Convolutional Network for Sleep Posture Detection

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 92.39% 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.Clinical Relevance— This sleep posture detection potentially suits diverse populations for long-term, at home settings.