## 2 Background and Motivation  
Contactless physiological monitoring systems working at a remote distance have attracted much attention over the past decades. Especially after the COVID-19 pandemic in early 2020, there has been an increased demand for contactless physiological monitoring systems for health monitoring/screening at a safe distance to reduce the spread of viruses. Out of many potential physiological parameters, heart rate and respiration rate have attracted much attention. Table 1 briefly summarizes the characteristics of various approaches in monitoring HR/RR. To date, most contactless HR/RR measurement systems rely on photoplethysmography (PPG) using RGB (red-green-blue) cameras with visible light illumination [1; 2; 3; 4; 5; 6; 7; 8]. Several approaches, such as the skin-tone or blind-source separation-based algorithm, are proposed for the removal of external artifacts and considerably improve the reliability of HR measurement [9; 10; 11; 12; 13; 14; 15; 16]. However, in some application scenarios such as vehicle driver monitoring or patient monitoring in hospitals, RGB camera-based systems face challenges from fluctuating or very low background illumination [17]. Moreover, because melanin, which gives rise to skin color, has \(-5\)X higher absorption in visible wavelength of 530 nm as compared to 850 nm [18], the performance of RGB camera-based systems can vary among users of different skin colors [6; 10; 17]. By using near-infrared illumination, such skin color dependence can be mitigated to achieve more uniform performance among wider demographics. Due to the benefits of being independent of background illumination and users' skin color, remote HR and RR monitoring systems using a near-infrared light source can be a valuable alternative. Over the past few years, HR/RR measurement systems using near-infrared cameras or even RGB/Depth cameras (such as Microsoft Kinect) [19; 20; 21; 22; 23; 24; 28] have been studied to show their capability of measuring HR/RR. In this study, we propose a system that \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System** & **Signal** & **Contactless** & **Skin Color** & **Ambient Light** & **Direct * RR** \\  & **Strength** & **Contactless** & **Independent** & **Independent** & **Measurement** \\ \hline Wearable EKG (Polar H10 belt) & ++ & \% & \% & \% & \% \\ Pulse oximente ([PERSON] & + & \% & \% & \% & \% \\ RGB Camera [1; 2; 3; 4; 5; 6; 7; 8] & = & \% & \% & \% & \% \\ Infrared Camera [19; 20; 21; 22; 23] & - & \% & \% & \% & \% \\ ToF Camera [24], [This] & - & \% & \% & \% & \% \\ Doppler Radar [25] & - & \% & \% & \% & \% \\ \hline \hline \end{tabular}  
* A direct measurement measures chest movement meanwhile an indirect RR measurement uses the modulation of the HR signal to extract RR [26; 27]: +/=/- indicates system has advantage/neutral/disadvantage in certain category; / \(\blacksquare\) indicates system has certain capability.  
\end{table} 
Table 1: Comparison of HR measuring devices. leverages the existing ToF cameras in many consumer electronics and vehicles to perform HR and RR monitoring. The ToF camera used in this study is an indirect ToF using a modulated VCSEL laser as its light source [30]. The benefit of this type of ToF camera is that it can extract the grayscale and depth information at the same time. The grayscale image is directly acquired through the exposure of the imaging sensor while the depth image is acquired by examining the phase change of the returned light [30]. Compared to previous studies, one of the main advantages and technical advancements of this study is that we demonstrate a method that combines both the depth and grayscale intensity information from the ToF camera to compensate for motion artifacts. Motion artifacts are known to be a challenging issue for contactless PPG systems, but even more so for systems that operate in the near-infrared wavelengths because the absorption by hemoglobin is much weaker in the near-infrared compared to visible wavelengths [31; 32].