非接触式UWB传感的生命体征检测分析.pdf

第 21 卷 第 4期 软件导刊 Vol. 21 No. of the feature vector transformed by in­
trinsic mode functions component is trained by GA-BP neural network，and Bayesian regularization is used as the training function of BP to re­
construct the cardiopulmonary signal, which is compared with the original EEMD reconstructed signal. The simulation results show that under
different signal-to-noise ratios，signals extracted by GA-BP neural network has higher consistency with the actual results，and can effectively
improve the extraction accuracy of respiratory and heartbeat signals.
Key Words：ultra wide band radar; ensemble empirical mode decomposition; GA-BP neural network; Bayesian regularization; signal pro­
cessing
或心理异常反应，使测量结果产生误差;其次，当受试者大
0 引言 面积皮肤受损时，接触式检测技术很难获取有效的生理信
号。因此，非接触式生命体征检测技术应运而生
随着现代社会的不断发展，越来越多的人们注重身体 基于雷达信号的非接触式生命体征检测技术是一项
健康的维持与监控。生命体征是评估身体健康的重要指 跨领域的融合技术。在地震灾害中，该技术可以检测废墟
标，主要包括体温、脉搏、呼吸和血压等。目前临床主要采 下是否有幸存人员及其生理状况；在家庭健康领域中，该
用接触式检测技术监测生命体征，如监护仪、心电图等，原