This study proposes a method for radar-based identification of individuals using a combination of their respiratory and heartbeat features. In the proposed method, the target individual's respiratory features are extracted using the modified raised-cosine-waveform model and their heartbeat features are extracted using the mel-frequency cepstral analysis technique. To identify a suitable combination of features and a classifier, we compare the performances of nine methods based on various combinations of three feature vectors with three classifiers. The accuracy of the proposed method in performing individual identification is evaluated using a 79-GHz millimeter-wave radar system with an antenna array in two experimental scenarios and we demonstrate the importance of use of the combination of the respiratory and heartbeat features in achieving accurate identification of individuals. The proposed method achieves accuracy of 96.33% when applied to a five-day dataset of six participants and 99.39% when applied to a public one-day dataset of thirty participants.
Transformer-Based Heartbeat Monitoring with FMCW Radar Under Random Body Motion
Dept. of Information Engineering, University of Pisa（比萨大学信息工程系）；Radar and Surveillance Systems (RaSS) National Laboratory, CNIT（CNIT雷达与监视系统（RaSS）国家实验室）
Radar-based Respiratory Rate Monitoring in Standing Position
使用雷达以非接触式非侵入性方法估计人的生命体征在医疗领域中提供了方便的方法，以容易且快速地进行多次健康检查。除了在坐着和睡觉时进行监测外，站立姿势也引起了工业和医疗领域的兴趣。然而，由于身体为了平衡而引起的微运动可能导致错误的呼吸率估计，因此更具挑战性。在这项工作中，我们专注于测量一个站立的人的呼吸频率准确的重呼吸检测和估计的能力。介绍并比较了多种估计方法，包括谱估计、基于深度学习的方法以及采用卡尔曼滤波的自适应峰值选择。与Vernier Go Direct \textsuperscript{\textregistered}呼吸带相比，最新技术显示出最佳性能，绝对误差率为1.5 bpm。
Recover from Horcrux: A Spectrogram Augmentation Method for Cardiac Feature Monitoring from Radar Signal Components
School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；Faculty of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电气工程学院）；Thrust of Artificial Intelligence and Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能学科、智能交通学科）；Institute of Deep Perception Technology, JITRI（江苏产业技术研究院深度感知技术研究所）
