计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211000182-6.doi: 10.11896/jsjkx.211000182
曾梓琳1, 胡志刚1,3, 尚鹏2, 王新征1, 付东辽1
ZENG Zi-lin1, HU Zhi-gang1,3, SHANG Peng2, WANG Xin-zheng1, FU Dong-liao1
摘要: 基于视频的非接触式面部心率检测易受到环境光和运动伪迹的干扰,检测心率结果的准确度低。针对上述问题,提出了一种集合经典模态分解(Ensemble Empirical Mode Decomposition,EEMD)和标准欧几里得距离相结合的自适应阈值化去噪方法,降低了外界干扰,提高了准确度。首先从视频录制的RGB图像模型中选取绿色(G)通道像素均值作为PPG(Photo Plethysmo Graphy)信号,然后用滤波器对信号进行预处理,消除心率范围外的信号;然后将EEMD与标准欧几里得距离相结合,对固有模态函数进行阈值化处理并重构;最后用傅里叶变换进行功率谱分析来计算心率值。实验结果表明,与基于小波变换和基于自适应集合经典模态分解的方法相比,所提方法在非接触式面部心率检测去噪中有更好的稳定性和准确性,提高了心率检测的鲁棒性,适用于日常非接触式实时心率监测。
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