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基于强大的联合视频的远程生理测量,用于异构的多源数据.

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    联合学习 (FL) 解决了远程光电显微镜 (rPPG) 中的隐私和数据传输成本. 我们的FedGRC框架解决了多来源的rPPG数据异质性,以改善非接触生理测量.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 计算机科学 计算机科学
    • 信号处理 信号处理

    背景情况:

    • 远程光电显微镜 (rPPG) 能够使用面部视频进行非接触式生理监测.
    • 目前的rPPG方法面临数据传输成本和隐私问题方面的挑战.
    • 联合学习 (FL) 提供了一个解决方案,但在多源rPPG数据中与跨域异质性作斗争.

    研究的目的:

    • 解决隐私和跨领域异质性的挑战,为远程光电系统学提供联合培训.
    • 提出一个新的联合学习框架,FedGRC,用于有效的多源rPPG数据分析.
    • 从输入和输出领域的角度来描述多源rPPG数据的异质性.

    主要方法:

    • 从输入和输出领域特征多源rPPG数据异质性.
    • 在联合学习框架内引入伪标签技术.
    • 开发了FedGRC,使用手工制作的rPPG方法进行自动梯度调整校准和伪标签对齐.

    主要成果:

    • 通过梯度调整校准,FedGRC有效地减轻了输入域差异.
    • 伪标签使输出域对齐,并减少跨数据集的标签不一致.
    • 在6个公共数据集中验证后,FedGRC在现有方法上展示了显著的优势.

    结论:

    • 美联储GRC框架成功地解决了多源rPPG数据中的隐私保护和异质性挑战.
    • 这种方法提高了无接触生理测量的联合学习的可行性.
    • FedGRC提供了一个强大的解决方案,用于隐私保护和异质的rPPG数据分析.