Two-Step Deep Learning for Estimating Human Sleep Pose Occluded by Bed Covers

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

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.

Occlusion-robust Sleep Posture Detection using Body Rolling Motion in a Video

It has been reported that the monitoring of sleep postures is useful for the treatment and prevention of sleep diseases such as obstructive sleep apnea and heart failure. Camera-based sleep posture detection is attractive for the nature of comfort and convenience of use. However, the main challenge is to detect postures from images of the body that are occluded by bed sheets or covers. To address this issue, we propose a novel occlusion-robust sleep posture detection method exploiting the body rolling motion in a video. It uses the head orientation to indicate the posture direction (supine, left or right lateral), triggered by the full-body rolling motion (as a sign of posture change). The experimental results show that our proposed method, as compared with the state-of-the-art approaches such as skeleton-based (MediaPipe) and full-image ResNet based methods, obtained clear improvements on sleep posture detection with heavy body occlusions, with an averaged precision, recall and F1-score of 0.974, 0.993 and 0.983, respectively. The next step is to integrate the sleep posture detection algorithm into a camera-based sleep monitoring system for clinical validations.