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    科学领域:

    • 生物医学工程 生物医学工程
    • 人工智能的人工智能
    • 计算机视觉 计算机视觉

    背景情况:

    • 远程光电脉扫描 (rPPG) 能够实现非接触式心率监测.
    • 现有的rPPG模型在现实的,不受约束的环境中往往表现不佳.
    • 需要强大的rPPG数据集来捕捉自然主义的受试者行为.

    研究的目的:

    • 在现实的条件下评估对比-物理人工智能模型用于远程光电显微镜 (rPPG).
    • 引入和利用新的CAMVISIM_LAB数据集用于rPPG研究.
    • 使用人工智能模型提高心率估计的准确性.

    主要方法:

    • 开发和使用CAMVISIM_LAB数据集,以自然主义参与者运动和互动为特色.
    • 对CAMVISIM_LAB,UBFC-RPPG和PURE数据集的对比-物理模型的评估.
    • 模型培训策略的比较分析:从零开始,预培训和微调.

    主要成果:

    • 在CAMVISIM_LAB上进行初始对比物理训练时,平均绝对误差 (MAE) 为每小时7.7 bpm.
    • 使用预先训练的模型将MAE提高到5.8bpm.
    • 使用预训练重量和CAMVISIM_LAB数据进行微调,实现了1.0bpm的最新MAE,超过了PURE数据集的结果.

    结论:

    • 当对CAMVISIM_LAB数据集进行微调时,Contrast-Phys模型在现实的rPPG应用中显示出卓越的性能.
    • CAMVISIM_LAB数据集为开发和验证强大的rPPG算法提供了宝贵的资源.
    • 人工智能驱动的rPPG具有在各种环境中准确,非侵入性心率监测的巨大潜力.