Non-contact vital sign health monitoring based on FMCW millimeter wave radar has received widespread attention because it can unobtrusively provide information about individuals' physical and mental states. Particularly, by continuously measuring a person's instantaneous heart rate over time, the activity level of the autonomic nervous system can be estimated. However, existing studies on non-contact measurements using millimeter wave radar typically focus only on heart rate variability under a single posture. This paper proposes a non-contact vital sign monitoring algorithm that integrates behavior recognition and heartbeat signal reconstruction. The algorithm utilizes millimeter wave radar to identify three common postures in daily life: standing, sitting, and lying down. Subsequently, it non-intrusively measures the heartbeat waveforms and conducts HRV analysis from the reconstructed heartbeat waveforms. In the study, the errors in HRV of RMSSD were 3.5ms for the sitting posture, while it is 5.8ms in the lying position, and 10.9ms in the standing posture.
Nowadays, daily vital sign monitoring can effectively prevent chronic diseases and has a very important position in the field of medical research. However, the monitoring method is basically contact-based, which causes inconvenience to the daily life of users. This work presents a set of integrated real-time monitoring device system for respiration, pulse, and blood pressure based on the characteristics of non-contact and wide range of milli-meter wave radar. The device obtains data by monitoring the wrist of the human body in a relaxed state. Through image processing and machine learning, the respiration and pulse waveforms with good accuracy are separated, and the obtained blood pressure value also has a certain accuracy. The milli-meter wave radar itself has the advantages of low power, simple circuit, and good integration, so the device has a great market prospect.
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