Pressure-Sensor-Based Sleep Status and Quality Evaluation System

Abstract

Sleep quality evaluation is a major approach to clinical diagnosis of different types of sleep andmental disturbances.How to render it possible for test subjects to complete sleep status detection and quality assessment in a natural environment is what this paper mainly highlights. Clinically, sleep disturbances are analyzed by the so-called Polysomnography (PSG). However, due to limitations in space and high cost, as well as the fact that test subjects must wear various physiological detection equipment during the test, the collected signal is susceptible to interference due to uncomfortable sensations. As such it usually does not reflect the realistic situation of the subject. For this reason,we propose in this paper a pressure-sensor-based smart mattress to realize sleep status detection and quality evaluation. Regarding sleep posture recognition, a proposed convolutional neural network (CNN) model in conjunction with a pressure distribution image formed by the pressure sensing matrix are applied. With respect to assessing the subjects’ time in bed, a support vectormachine (SVM) is used to determine sitting or lying postures, and further recognize the actual time in bed. As for sleep quality assessment, Fuzzy inference is adopted in this paper base on a set of four predefined sleep parameters. Compared with some of prior arts, the proposed systemdoes not require the test subjects to wear any equipment, and as such the subjects can complete the test in a natural environment. Experimental results show that the accuracy of the proposed SVM classifier for differentiating sitting and lying posture and that of CNN model for the recognition of four different sleep postures can be up to 99.986% and 96.987%, respectively. With the proposed model, precise sleep parameters, including time in bed, number of times of bed-leaving, number of times of body movements all night, standard deviation of time interval between body movements, and sleep posture can be provided. Moreover, the system does not use devices such as microphones or surveillance cameras to collect the data of the test subjects; thus, there is no concern about infringing the privacy of the subjects. It is believed that the system will be of considerable help and serve as an aid to clinical diagnosis of sleep disturbance.