Sleep Position Classification From A Depth Camera Using Bed Aligned Maps

Sleep position is an important feature used to assess the quality and quantity of an individual's sleep. Furthermore, it is related to sleep disorders like sleep apnoea and snoring, and needs to be tracked in nursery homes to avoid pressure ulcers. Therefore, a gravity sensor attached to the chest is generally used to register body position during sleep studies. We suggest a non-intrusive and cost-efficient approach to detect the sleep position based on a single depth camera. Compared to alternative state-of-the-art approaches, ours require no calibration, and has been evaluated on a real setting comprising 78 patients from a sleep laboratory. We use the Bed Aligned Maps to extract a low resolution descriptor from a depth map which is aligned to the bed position, We perform classification using Convolutional Neural Networks, achieving an accuracy of 94.0%, thus outperforming current state-of-the-art algorithms and even the contact sensor from the sleep laboratory which achieves an accuracy of 91.9%.