Dynamic Vital Sign estimation for Multiple Persons using mmWave technology

Master thesis (2022)

Authors

D. den Hoedt Electrical Engineering, Mathematics and Computer Science

Contributors

Marco Zuniga Embedded Systems - EEMCS (mentor)
F. Fioranelli Microwave Sensing, Signals & Systems - EEMCS (graduation committee member)
C.J. Ramsey Tera-Hertz Sensing - EEMCS (graduation committee member)
T.G. Goos Medical Instruments & Bio-Inspired Technology - Mechanical, Maritime and Materials Engineering (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Dirk den Hoedt
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Published Date

14-10-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

When a patient is in a hospital, it is very important to monitor their vital signs. Doctors and nurses use this information to assess the condition of the patient. Most of the existing vital signs measurement devices need physical contact with the patient. This thesis focuses on a non-contact vital signs estimation method. Using a mmWave radar, one or more persons in the view of the sensor are being monitored. This monitoring consists of finding the chest region of a person, and monitoring this chest for vibrations. These vibrations are caused by breathing in and out, and the beating of the heart. Using signal processing, these vibrations can be converted to a heart rate and a respiration rate.

This thesis is about getting insight in the already available options regarding vital signs monitoring, programming the Texas Instruments IWR6843ISK mmWave radar module to estimate the vital signs of multiple persons and validating this project against trusted vital signs monitors.

The implemented solution which followed from this project is able to track multiple persons inside the radar view, and is able to measure the vital signs for up to four persons in real-time. The mean accuracy gained for one person heart rate estimation is 10.8%, the mean accuracy gained for one person respiration rate estimation is 7.6%. The mean observed accuracy for multiple person heart rate estimation is 13.4%, the multiple person respiration rate mean accuracy is 10.6%.