Pressure-Sensor-Based Sleep Status and Quality Evaluation System

Sleep quality evaluation is a major approach to clinical diagnosis of different types of sleep and mental disturbances. How to render it possible for test subjects to complete sleep status detection and quality assessment in a natural environment is what this article 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 types of 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 article 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 is applied. With respect to assessing the subjects’ time in bed, a support vector machine (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 article based on a set of four predefined sleep parameters. Compared with some of prior arts, the proposed system does 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 the 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, the number of times of bed leaving, the 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.