适应性热成像信号分析用于实时非侵入性呼吸速率监测
Riska Analia1,2, Anne Forster3, Sheng-Quan Xie1
1School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9LN, UK.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究引入了一种适应性热成像系统,用于在边缘设备上无接触式呼吸率监测. 它在现实条件下准确估计呼吸率,提供保护隐私的生命信号解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 在远程医疗保健中,开发无接触和保护隐私的生命体征监测系统至关重要.
- 现有的方法通常需要专门的硬件或外部计算,限制了实际部署.
- 热成像为生理监测提供了一种非侵入性的方法.
研究的目的:
- 通过使用热成像来呈现一种适应性,非接触性和保护隐私的呼吸频率监测系统.
- 为了实现嵌入式边缘硬件的实时操作,以实现实用的生命体征监测.
- 在各种现实条件下评估系统的准确性和稳定性.
主要方法:
- 使用TOPDON TC001热摄像头和NVIDIA Jetson Orin Nano进行边缘处理.
- 采用基于YOLO的探测器来定位鼻孔,并使用卡尔曼波器来稳定ROI.
- 从温度数据中提取呼吸信号,并使用自适应中位数-MAD歇斯底里算法进行相位检测分析.
主要成果:
- 在各种不同的实验条件下实现了0.57±0.36 BPM的平均绝对误差 (MAE) 和0.64±0.42 BPM的根平均平方误差 (RMSE).
- 尽管运动,不同距离 (高达2.0米) 和热漂移,但表现出稳定的准确性.
- 与传统的以峰值为基础的和快速里埃变换 (FFT) 谱分析方法相比,显著降低了错误.
结论:
- 证实了使用嵌入式边缘设备上的低分辨率热传感器精确和强大的呼吸率估计的可行性.
- 通过YOLO,卡尔曼过和自适应MAD歇斯底里的综合方法,提供了一种高效且保护隐私的解决方案.
- 该系统适用于各种现实环境中的非侵入性生命体征监测.
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