Gharamohammadi Ali, Bagheri Mohammad Omid, Abu-Sardanah Serene, Riad Michael M Y R, Abedi Hajar, Ansariyan Ahmad, Wang Kang, Saragadam Ashish, Chumachenko Dmytro, Abhari Shahabeddin, Morita Plinio Pelegrini, Khajepour Amir, Shaker George
Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada.
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.
Sci Rep. 2025 Jan 9;15(1):1392. doi: 10.1038/s41598-024-80062-5.
The integration of radar technology into smart furniture represents a practical approach to health monitoring, circumventing the concerns regarding user convenience and privacy often encountered by conventional smart home systems. Radar technology's inherent non-contact methodology, privacy-preserving features, adaptability to diverse environmental conditions, and high precision characteristics collectively establish it a compelling alternative for comprehensive health monitoring within domestic environments. In this paper, we introduce a millimeter (mm)-wave radar system positioned strategically behind a seat, featuring an algorithm capable of identifying unique cardiac waveform patterns for healthy subjects. These patterns are characterized by two peaks followed by a valley in each cycle, which can be correlated to Electrocardiogram (ECG), enabling effective cardiac waveform monitoring. The provided algorithm excels in discerning variations in heart patterns, particularly in individuals with prolonged corrected QT intervals, by minimizing high frequency breathing interference and ensuring accurate pattern recognition. Additionally, this paper addresses the influence of body movements in seated individuals, conducting a comprehensive study on heart rate variability and estimation. Experiment results demonstrate a maximum interbeat intervals (IBI) error of 30 milliseconds and an average relative error of 4.8% in heart rate estimation, showcasing the efficacy of the proposed method utilizing variational mode decomposition and a multi-bin approach.
将雷达技术集成到智能家具中是一种用于健康监测的切实可行的方法,避免了传统智能家居系统常常遇到的用户便利性和隐私方面的问题。雷达技术固有的非接触式方法、隐私保护特性、对各种环境条件的适应性以及高精度特性,共同使其成为家庭环境中进行全面健康监测的极具吸引力的选择。在本文中,我们介绍了一种毫米波雷达系统,该系统被战略性地放置在座椅后方,其算法能够识别健康受试者独特的心脏波形模式。这些模式的特征是每个周期有两个峰值,随后是一个波谷,这可以与心电图(ECG)相关联,从而实现有效的心脏波形监测。所提供的算法擅长辨别心脏模式的变化,特别是对于校正QT间期延长的个体,通过最小化高频呼吸干扰并确保准确的模式识别来实现。此外,本文探讨了坐姿个体身体运动的影响,对心率变异性和估计进行了全面研究。实验结果表明,在心率估计中,最大心跳间期(IBI)误差为30毫秒,平均相对误差为4.8%,展示了利用变分模态分解和多箱方法的所提方法的有效性。