Electromagnetic radars have been shown potentially to be used for remote sensing of biosignals in a more comfortable and easier way than wearable and contact devices. While there is an increasing interest in using radars for health monitoring, their performance has not been tested and reported either in practical scenarios or with acceptable low errors. Therefore, we use a frequency modulated continuous wave (FMCW) radar operating at 77 GHz in a bedroom environment to extract the respiration and heart rates of a patient, who is used to lying down on the bed. Indeed, the proposed signal processing contains advanced phase unwrapping manipulation, which is unique. In addition, the results are compared with a reliable reference sensor. Our results show that the correlations between the reference sensor and the radar estimates are in 94% and 80% for breathing and heart rates, respectively.INDEX TERMS Breathing rate monitoring, FMCW radar, heart rate monitoring, Hexoskin, mm-wave, non-contact monitoring, phase analysis, remote sensing, vital signs, TI.
In this paper, we use a low-cost low-power mm-wave frequency modulated continuous wave (FMCW) radar for the in-vehicle occupant detection. We propose an algorithm using Capon filter for the joint range-azimuth estimation. Then, the minimum necessary features are extracted to train machine learning classifiers to have reasonable computational complexity while achieving high accuracy. In addition, experiments were carried out in a minivan to detect occupancy of each row using support vector machine (SVM). Finally, our proposed system achieved 97.8% accuracy on average in finding the defined scenarios. Moreover, The system can correctly identify if the vehicle is occupied or not with 100% accuracy.
There are many patients who require continuous monitoring of vital signs and their sleep position such as bedbound patients and hospitalized patients. Also, in some cases, like COVID-19, it is critical for a caregiver to keep a safe distance to the patient. For remote monitoring, radar technologies have been shown to be promising. Thus, in this paper, we present a novel solution for the remote breath and sleep position monitoring by using a multi-input-multi-output (MIMO) radar. Our proposed system could monitor a number of people simultaneously, and therein we use a high-resolution direction of arrival (DOA) detection for finding close targets. Furthermore, the sleep position of each target is determined using a support vector machine (SVM) classifier. The breath analysis involves designing an optimum filter for estimating both the breathing rate and the noiseless breathing waveform. Furthermore, we tested the system by hand-made targets and real human targets. The radar placed in a bedroom environment above a bed where two subjects were sleeping next to each other. For the breathing rate, the accuracy of the radar is more than 97% for human subjects compared with a reference sensor. Also, the sleep position correct detection is more than 83%.
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