集成技术2025，Vol.14Issue(2)：33-45,13.DOI:10.12146/j.issn.2095-3135.20231030001
睡眠监控中基于毫米波雷达心脏信号的非接触身份识别:一种深度卷积模型
Non-contact Identification Recognition Based on Millimeter-Wave Radar Cardiac Signals During Sleep Monitoring:A Deep Convolution Model
摘要
Abstract
Non-contact vital sign monitoring using millimeter-wave radar offers continuous and discreet identification.Cardiac motion is influenced by various complex factors,making it challenging to capture characteristic waveform information.To address this,the study employs millimeter-wave radar transmitting frequency modulated continuous waves to monitor and record cardiac data during sleep.Additionally,the paper proposes a deep convolutional neural network(CNN)-based identity recognition method using one-dimensional time-series radar signals of cardiac motion.The performance of this method is compared with three deep learning algorithms:long short-term memory Network,InceptionTime,and LSTformer.The final classification accuracies of all models exceed 85%on a dataset of heart signals collected in a resting state in the laboratory.Among the models,InceptionTime achieves the highest accuracy but requires the longest processing time.The long short-term memory and LSTformer models exhibit lower accuracy but faster processing.The CNN model proposed in this study demonstrates comparable accuracy to InceptionTime,while requiring less computational time,thus balancing accuracy and efficiency.关键词
毫米波雷达/深度学习/身份识别Key words
millimeter wave radar/deep learning/identification recognition分类
电子信息工程引用本文复制引用
段玉龙,胡巍,黄毅,陈垦..睡眠监控中基于毫米波雷达心脏信号的非接触身份识别:一种深度卷积模型[J].集成技术,2025,14(2):33-45,13.基金项目
腾讯技术公益创投计划 This work is supported by Tencent Technology Philanthropy and Venture Capital Program （）
