Print Email Facebook Twitter Dynamic Vital Sign estimation for Multiple Persons using mmWave technology Title Dynamic Vital Sign estimation for Multiple Persons using mmWave technology Author den Hoedt, Dirk (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Embedded and Networked Systems) Contributor Zuniga, Marco (mentor) Fioranelli, F. (graduation committee) Ramsey, C.J. (graduation committee) Goos, T.G. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Embedded Systems Date 2022-10-14 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%. Subject Vital signsIWR6843ISKVital signs estimationSignal ProcessingEmbedded SystemsReal-timeTexas InstrumentsMonitoringHeart rateRespiration rate To reference this document use: http://resolver.tudelft.nl/uuid:63813064-dee6-41bc-b4f6-32ea42cb9ff3 Part of collection Student theses Document type master thesis Rights © 2022 Dirk den Hoedt Files PDF TUD_ENS_MSc_Thesis_Dirk_d ... _Hoedt.pdf 4.18 MB Close viewer /islandora/object/uuid:63813064-dee6-41bc-b4f6-32ea42cb9ff3/datastream/OBJ/view