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Contactless Vital Signs Measurement System Using RGB-Thermal Image Sensors and Its Clinical Screening Test on Patients with Seasonal Influenza

机译:RGB热图像传感器的非接触式生命体征测量系统及其对季节性流感患者的临床筛查测试

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摘要

In the last two decades, infrared thermography (IRT) has been applied in quarantine stations for the screening of patients with suspected infectious disease. However, the fever-based screening procedure employing IRT suffers from low sensitivity, because monitoring body temperature alone is insufficient for detecting infected patients. To overcome the drawbacks of fever-based screening, this study aims to develop and evaluate a multiple vital sign (i.e., body temperature, heart rate and respiration rate) measurement system using RGB-thermal image sensors. The RGB camera measures blood volume pulse (BVP) through variations in the light absorption from human facial areas. IRT is used to estimate the respiration rate by measuring the change in temperature near the nostrils or mouth accompanying respiration. To enable a stable and reliable system, the following image and signal processing methods were proposed and implemented: (1) an RGB-thermal image fusion approach to achieve highly reliable facial region-of-interest tracking, (2) a heart rate estimation method including a tapered window for reducing noise caused by the face tracker, reconstruction of a BVP signal with three RGB channels to optimize a linear function, thereby improving the signal-to-noise ratio and multiple signal classification (MUSIC) algorithm for estimating the pseudo-spectrum from limited time-domain BVP signals within 15 s and (3) a respiration rate estimation method implementing nasal or oral breathing signal selection based on signal quality index for stable measurement and MUSIC algorithm for rapid measurement. We tested the system on 22 healthy subjects and 28 patients with seasonal influenza, using the support vector machine (SVM) classification method. : The body temperature, heart rate and respiration rate measured in a non-contact manner were highly similarity to those measured via contact-type reference devices (i.e., thermometer, ECG and respiration belt), with Pearson correlation coefficients of 0.71, 0.87 and 0.87, respectively. Moreover, the optimized SVM model with three vital signs yielded sensitivity and specificity values of 85.7% and 90.1%, respectively. : For contactless vital sign measurement, the system achieved a performance similar to that of the reference devices. The multiple vital sign-based screening achieved higher sensitivity than fever-based screening. Thus, this system represents a promising alternative for further quarantine procedures to prevent the spread of infectious diseases.
机译:在过去的二十年中,红外热成像(IRT)已用于隔离站,以筛查疑似传染病患者。但是,由于仅监测体温不足以检测出感染的患者,因此采用IRT的发烧筛查程序灵敏度较低。为了克服基于发烧的筛查的弊端,本研究旨在开发和评估使用RGB热图像传感器的多个生命体征(即体温,心率和呼吸率)测量系统。 RGB摄像机通过从人脸区域吸收光的变化来测量血容量脉冲(BVP)。 IRT用于通过测量伴随呼吸的鼻孔或嘴附近的温度变化来估计呼吸速率。为了实现稳定可靠的系统,提出并实施了以下图像和信号处理方法:(1)RGB热图像融合方法可实现高度可靠的面部关注区域跟踪;(2)心率估计方法包括用于减少由面部追踪器引起的噪声的锥形窗口,具有三个RGB通道的BVP信号重构以优化线性功能,从而改善了信噪比和用于估计伪噪声的多信号分类(MUSIC)算法。在15 s内从有限的时域BVP信号中获取频谱,以及(3)一种呼吸速率估计方法,该方法基于信号质量指标进行稳定的测量并采用MUSIC算法进行快速测量,从而实现鼻腔或口腔呼吸信号的选择。我们使用支持向量机(SVM)分类方法在22名健康受试者和28名季节性流感患者中测试了该系统。 :以非接触方式测量的体温,心率和呼吸率与通过接触式参考设备(即温度计,心电图和呼吸带)测量的体温,心率和呼吸率高度相似,皮尔逊相关系数分别为0.71、0.87和0.87 , 分别。此外,具有三个生命体征的优化的SVM模型产生的敏感性和特异性值分别为85.7%和90.1%。 :对于非接触式生命体征测量,该系统实现了与参考设备相似的性能。与基于发烧的筛查相比,基于多个生命体征的筛查具有更高的敏感性。因此,该系统代表了进一步检疫程序以防止传染病传播的有希望的替代方法。

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