Deep Residual Networks for Sleep Posture Recognition With Unobtrusive Miniature Scale Smart Mat System

Sleep posture, as a crucial index for sleep quality assessment, has been widely studied in sleep analysis. In this paper, an unobtrusive smart mat system based on a dense flexible sensor array and printed electrodes along with an algorithmic framework for sleep posture recognition is proposed. With the dense flexible sensor array, the system offers a comfortable and high-resolution solution for long-term pressure sensing. Meanwhile, compared to other methods, it reduces production costs and computational complexity with a smaller area of the mat and improves portability with fewer sensors. To distinguish the sleep posture, the algorithmic framework that includes preprocessing and Deep Residual Networks (ResNet) is developed. With the ResNet, the proposed system can omit the complex hand-crafted feature extraction process and provide compelling performance. The feasibility and reliability of the proposed system were evaluated on seventeen subjects. Experimental results exhibit that the accuracy of the short-term test is up to 95.08% and the overnight sleep study is up to 86.35% for four categories (supine, prone, right, and left) classification, which outperform the most of state-of-the-art studies. With the promising results, the proposed system showed great potential in applications like sleep studies, prevention of pressure ulcers, etc.