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摘要: 为了在非接触条件下检测受试者的各项生理参数，本文设计了一种基于成像式光电容积描记技术，从手机录制的人脸视频中估算生理参数的方法。首先，提出了“小波变换-主成分分析-盲源分离”算法，用于提取出高信噪比的RGB三通道脉搏波信号。然后，分别从频域和时域角度对绿色通道信号进行处理，估算出心率值和呼吸率值；对红蓝通道的脉搏波信号进行处理，并结合血氧仪检测的血氧饱和度结果，进行数据拟合，从而找到从面部视频中估算血氧饱和度值的最佳线性方程。最后，对比了自然光下各生理参数的估算结果误差，分析了在3种光照环境下各参数的估算结果。结果表明：3种光照环境下得到的心率平均误差为0.5512次/min，呼吸率平均误差为−0.6321次/min，血氧饱和度平均误差为−0.2743%。综上，本文提出的非接触式生理参数估算方法精度高，具有普适性和稳定性，估算结果同标准仪器的测量结果具有高度一致性，可满足日常生理参数测量的需求。
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关键词:
- 成像式光电容积描记技术 /
- 非接触式 /
- 小波变换-主成分分析-盲源分离 /
- 心率 /
- 呼吸率 /
- 血氧饱和度
Abstract: Non-contact detection of various physiological parameters has attract great attention. In this paper, a method of estimating physiological parameters based on imaging photoplethysmography from videos of people’s faces recorded by mobile phone is proposed. First, a "wavelet transform-principal component analysis-blind source separation" algorithm is proposed to extract the video’s RGB three-channel pulse wave signal with a high signal-to-noise ratio. Then, the green channel signal is processed separately in the frequency and the time domains to estimate heart and respiratory rates. The pulse wave signals of the red and blue channels are processed, and combined with the oxygen saturation detected by an oximeter to perform data fitting, the best linear equation for estimating the oxygen saturation value from the facial video is found. Finally, the error of the estimation results of various physiological parameters under natural light is compared, and the estimation results of each parameter under three lighting environments are analyzed. The results show that under the three lighting environments, the average error of heart rate detection is 0.5512 time/min, the average error of respiration rate is −0.6321 time/min , and the average error of oxygen saturation is −0.2743%. In summary, the non-contact physiological parameter estimation method proposed in this paper is highly accurate, universally applicable and stable. Its estimation results are highly consistent with the measurement result of standard instruments, which meets the needs of daily physiological parameter measurement. -
图 2 放大前、后IPPG源信号对比
Figure 2. Comparison of IPPG source signals before and after amplification
图 4 PCA降维后各成分信号
Figure 4. The signal of each component after PCA dimensionality reduction
图 9 实验采集示意图及装置图
Figure 9. Schematic diagram and real diagram of the experimental acquisition device
图 10 3种场景下心率估算结果比较
Figure 10. Comparison of heart rate estimation results in three scenarios
图 11 3种场景下心率结果Bland-Altman 一致性分析
Figure 11. Bland-Altman consistency analysis of heart rate results in three scenarios
图 12 3种场景下呼吸率估算结果比较
Figure 12. Comparison of respiratory rate estimation results in three scenarios
图 13 3种场景下呼吸率结果Bland-Altman 一致性分析
Figure 13. Bland-Altman consistency analysis of respiratory rate results in three scenarios
图 14 3种场景下SpO2估算结果比较
Figure 14. Comparison of SpO2 estimation results in three scenarios
图 15 3种场景下SpO2结果Bland–Altman 一致性分析
Figure 15. Bland-Altman consistency analysis of SpO2 results in three scenarios
表 1 自然光下各心率检测算法性能比较
Table 1. Performance comparison of various heart rate detection algorithms under natural light
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表 2 自然光下各呼吸率检测算法性能比较
Table 2. Performance comparison of various respiratory rate detection algorithms under natural light
方法 Me
(time·min−1)|Me|
(time·min−1)SDe
(time·min−1)RMSE
(time/min−1)Cor 文献[10] −0.58 2.54 3.98 4.02 0.61 本文方法 0.63 1.78 1.88 1.98 0.60
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表 3 自然光下各SpO2检测算法性能比较
Table 3. Performance comparison of SpO2 detection algorithms under natural light
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