Multi-target Vital Sign Detection by Fusion of Biological Radar and Convolutional Neural Network

Main Article Content

Hongbin Yuan
Chenyao Yuan
Huiqun Cao

Abstract

In order to address the increasing demand for vital sign detection, the author proposes a multi-target vital sign detection research that combines biological radar and convolutional neural network. Based on the fundamental architecture of convolutional neural networks (CNNs), the author combines classification-based CNN object detection techniques to develop a biological radar multi-target vital sign detection platform. The feasibility of this approach is confirmed through experiments, demonstrating the integration of biological radar and CNNs for multi-target vital sign detection. The experimental results indicate that the biological radar achieves a recognition accuracy of 96.1%, proving the effectiveness of the biological radar detection algorithm. The research on multi-target vital sign detection based on the fusion of biological radar and convolutional neural network is an effective auxiliary method that can provide reference for relevant researchers.

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Section
Speciai Issue - Deep Learning in Healthcare