Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 15; p. 4833
• Main Authors: Hu, Dikun, Gao, Weidong, Ang, Kai Keng, Hu, Mengjiao, Chuai, Gang, Huang, Rong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.08.2024
• Subjects: Adult; BCG; BCG vaccines; Female; Heart Rate - physiology; Humans; Male; model-based feature extraction; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; Neural networks; Neural Networks, Computer; Polysomnography - instrumentation; Polysomnography - methods; Posture - physiology; Respiration; Respiratory Rate - physiology; Sensors; Signal processing; Sleep - physiology; Sleep apnea syndromes; Sleep Apnea, Obstructive - diagnosis; Sleep Apnea, Obstructive - physiopathology; sleep posture detection; sparse sensor-based; spatiotemporal convolutional network; model-based feature extraction; sparse sensor-based; spatiotemporal convolutional network; sleep posture detection
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24154833

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S3CNN) for detecting sleep posture. This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S3CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S3CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.