基于远程光电缩图的心率测量先进的信号处理框架:集成自适应卡尔曼过与离散波幅转换
1Department of Electronic Engineering, Yeungnam University, Gyeongsan, South Korea.
PloS one
|January 20, 2026
概括
这项研究引入了一种新的信号处理框架,用于远程光电脉冲图 (rPPG),以精确测量心率 (HR),尽管存在运动和照明挑战. 通过DWT-RAKF方法,可以显著改善不同肤色的HR估计.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 光学传感传感器是什么?
背景情况:
- 远程光斑血学 (rPPG) 提供非接触式生命体征监测,但面临着诸如运动器件,变光和皮肤色调差异等挑战.
- 现有的rPPG方法由于信号质量问题和人口变化而难以准确,限制了它们的临床和非临床应用.
研究的目的:
- 开发一个先进的信号处理框架,以提高基于rPPG的心率 (HR) 估计的准确性和稳定性.
- 为了解决传统的rPPG方法的局限性,包括运动工件,环境光变化和各种肤色.
主要方法:
- 提出了一个DWT-RAKF框架,集成离散波波变换 (DWT) 进行无声化和基于残余的自适应卡尔曼过 (RAKF) 进行时间一致性和运动器件减少.
- 实施了多通道融合策略,采用双阶段带通过,以隔离HR信号和丢弃噪声.
- 评估了公共 (PURE) 和定制数据集的框架,这些数据集具有不同的肤色和自然照明条件.
主要成果:
- 在PURE数据集上,DWT-RAKF框架实现了0.72bpm的平均绝对误差 (MAE) 和1.14bpm的根平均平方误差 (RMSE),超过了最先进的方法.
- 在对定制数据集的现实世界测试中,该算法展示了低MAE的0.94 bpm对于肤浅的人和1.11 bpm对于深棕色的人.
- 拟议的过策略有效地实现了不同肤色的实时HR测量.
结论:
- 该DWT-RAKF框架显著提高了使用rPPG的非接触式心率监测的准确性和可靠性.
- 拟议的方法在不同的人口群体和具有挑战性的环境条件中表现出强的性能.
- 这种先进的信号处理方法有望在各种环境中进行不引人注目的,长期的生命体征监测.
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