Albrecht Nils C, Langer Dominik, Krauss Daniel, Richer Robert, Abel Luca, Eskofier Bjoern M, Rohleder Nicolas, Koelpin Alexander
Institute of High-Frequency TechnologyTechnische Universität Hamburg 21073 Hamburg Germany.
Machine Learning and Data Analytics LabFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU) 91054 Erlangen Germany.
IEEE Open J Eng Med Biol. 2024 Jun 28;5:725-734. doi: 10.1109/OJEMB.2024.3420241. eCollection 2024.
In biomedical monitoring, non-intrusive and continuous tracking of vital signs is a crucial yet challenging objective. Although accurate, traditional methods, such as electrocardiography (ECG) and photoplethysmography (PPG), necessitate direct contact with the patient, posing limitations for long-term and unobtrusive monitoring. To address this challenge, we introduce the EmRad system, an innovative solution harnessing the capabilities of continuous-wave (CW) radar technology for the contactless detection of vital signs, including heart rate and respiratory rate. EmRad discerns itself by emphasizing miniaturization, performance, scalability, and its ability to generate large-scale datasets in various environments. This article explains the system's design, focusing on signal processing strategies and motion artifact reduction to ensure precise vital sign extraction. The EmRad system's versatility is showcased through various case studies, highlighting its potential to transform vital sign monitoring in research and clinical contexts.
在生物医学监测中,对生命体征进行非侵入式连续跟踪是一个至关重要但具有挑战性的目标。虽然心电图(ECG)和光电容积脉搏波描记法(PPG)等传统方法准确,但需要与患者直接接触,这对长期和非侵入式监测构成了限制。为应对这一挑战,我们引入了EmRad系统,这是一种创新解决方案,利用连续波(CW)雷达技术的能力对包括心率和呼吸频率在内的生命体征进行非接触式检测。EmRad通过强调小型化、性能、可扩展性以及在各种环境中生成大规模数据集的能力而脱颖而出。本文解释了该系统的设计,重点是信号处理策略和运动伪影减少,以确保精确提取生命体征。通过各种案例研究展示了EmRad系统的多功能性,突出了其在研究和临床环境中改变生命体征监测的潜力。