Remote Health Monitoring System for Bedbound Patients

In this paper, we present a novel solution for the remote breathing and sleep position monitoring by using a multi-input-multi-output (MIMO) radar. Our proposed system is able to monitor a number of people simultaneously, and therein we use a high-resolution direction of arrival (DOA) detection for finding closely separated targets. So, it effectively increases the number of target detection and reduces the cost by reducing the number of sensors. Furthermore, our proposed system is capable of identifying the sleep position of each monitored person by selecting appropriate target features and using a support vector machine (SVM) classifier. The breathing analysis involves designing an optimum filter for estimating both the breathing rate and the noiseless breathing waveform. In addition, we use the radar in a bedroom environment above a bed where two subjects sleep next to each other. The accuracy of the breathing monitoring subsystem is more than 97% for human subjects in the bedroom compared with a reference sensor. Also, the correct rate for sleep position detection is more than 83%.