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使用多区域光流分析进行非接触呼吸率估计的混合CNN光谱架构

Sreya Deb Srestha¹, Sungho Kim¹

¹Department of Electronic Engineering, Yeungnam University, Gyeongsan, South Korea.

|February 20, 2026

概括

这项研究开发了一种非接触式呼吸率 (RR) 监测系统,使用定制的CNN和光谱分析. 该方法准确地估计了不同人群的RR,为持续的医疗保健提供了可靠的解决方案.

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
生理监测生理监测

背景情况:

非接触式呼吸速率 (RR) 测量对于健康评估至关重要,但面临诸如运动器件,照明变化和各种皮肤色调等挑战.
现有的方法在现实场景中经常难以准确和通用.

研究的目的:

开发一个强大的,非接触框架,用于在不同人群中准确估计呼吸速率.
为了克服当前非接触式RR监控方法的局限性.

主要方法:

使用定制卷积神经网络 (CNN) 与光流信号的光谱分析相结合.
从六个解剖区域 (额头,脸,胸部上部,肩膀) 提取了呼吸诱导的运动.
用自适应卡尔曼过和基于SNR的加权融合进行了可靠的估计,并进行了广泛的数据增强以实现概括性.

主要成果:

拟议的方法实现了0.61-0.95每分钟呼吸 (bpm) 的平均平均误差 (MAE),跨越不同的皮肤色调和年龄.
该系统在临床相关范围内的高精度,即使在不同的照明条件下.

结论:

多区域CNN频谱框架为呼吸监测提供了可靠的,非接触的,实时的解决方案.
这项技术对持续医疗保健和远程医疗应用具有重大潜力.

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