Deep Learning Model-Based Turn-Over Intention Recognition of Array Air Spring Mattress

Abstract

Turn-over intention recognition of patient is crucial for the advancement of the intelligent nursing field. In this paper, a novel turn-over intention method is proposed based on array air spring mattress. For this method, the turn-over intention of a lying patient can be recognized by identifying the internal pressure distribution of array air springs. To begin with, the samples of turn-over intention are created experimentally, and then input into a model combining Variational Auto-Encoder and Generative Adversarial Network for the sample augmentation to address issues related to low accuracy and poor generalization caused by sample imbalance. Besides, the augmented dataset is conveyed into the Convolutional Neural Network model, for the detection of three states: left/right turn-over intentions and no intention. The research demonstrates that, the similarity of the left and right turn-over intention samples generated by VAE-GAN model is 90.13% and 91.01%, respectively. This increases the diversity of samples and is helpful for intention recognition. The recognition accuracy of the CNN model with sample augmentation is 98.04%, which is 13.4% higher than without sample augmentation. The proposed method is effective to turn-over intention recognition, by identifying the internal pressure distribution of array air spring mattress. The efficiency of intelligent nursing systems can be substantially improved, thus ensuring better patient care and safety.