Portable Real-Time System for Multi-Subject Localization and Vital Sign Estimation

A real-time non-contact vital sign detection system is developed by utilizing neural network-based detection, multi-object tracking, and direction of arrival (DoA) techniques. The DoA produces a spatial-based image, which is fed into the detector. The detector is a convolutional neural network (CNN)...

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Bibliographic Details
Main Authors: Rajagopal, Vijaysrinivas, Moadi, Abdel-Kareem, Fathy, Aly E., Abidi, Mongi A.
Format: Conference Proceeding
Language:English
Subjects:
Detectors
Error analysis
Estimation
Heart rate
Location awareness
machine learning
Pipelines
real-time
tracking
vital sign estimation
Wireless communication
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Description
Summary:A real-time non-contact vital sign detection system is developed by utilizing neural network-based detection, multi-object tracking, and direction of arrival (DoA) techniques. The DoA produces a spatial-based image, which is fed into the detector. The detector is a convolutional neural network (CNN), which produces a list potential subject locations. These locations are propagated and associated via a tracking method called BYTE. All of these methods allow the system to accurately localize and track subjects as well as improve the robustness of vital sign estimation for stationary, multi-subject scenarios. We demonstrate that this real-time system produces low error rates of less than 1 and 3 BPM for breathing and heart rate estimations respectively in both single and multi-subject scenarios. All this is done while maintaining an average of 14 FPS on a portable Jetson Xavier NX.
ISSN:2164-2974
DOI:10.1109/RWS55624.2023.10046315