Method of recognizing sleep postures based on air pressure sensor and convolutional neural network: For an air spring mattress

Abstract

The present study aimed to develop a sleep postures recognition system based on the hardness adjustment system for a specific air spring mattress. To the end, an air spring mattress prototype and its embedded system was manufactured. Then the supine and lateral postures were defined, and the sleep posture images generated by the relative change rate of air pressure matrix were filtered. At last, a convolutional neural network (CNN) model was proposed and analyzed by ablation experiment. Furthermore, the CNN model was compared with a CNN-SVM fusion model and a ResNet50 model to valid the performance. The results indicate that it is feasible to define sleep postures with the air pressure, and the images smoothed by a Gaussian filter contains significant features. The F1-score of the CNN model determined by the ablation experiment is 0.981, while the F1-score values of the CNN-SVM fusion model and the ResNet50 model are 0.932 and 0.954, respectively. Therefore, the generalization ability of the CNN model proposed outperformed the other two. Finally, the F1-score of the SSA-CNN model optimized by Sparrow Search Algorithm (SSA) increased to 0.992. It concludes that sleep posture recognition can be achieved using only the inherent structure of the air spring mattress without additional sensors, reducing the cost and complexity of the system. In addition, the air pressure signal can be processed by the proposed CNN model to recognize sleep postures with a high accuracy.