## 6 Summary  
In this study, we first present a non-distracting CVSM system that can monitor the HR and RR for both passengers and drivers in a vehicle using an indirect ToF camera. The ToF camera allows us to mitigate two major challenges for measuring HR in a moving vehicle, namely, varying illumination level and motion artifacts [11]. Compared to a conventional 2D camera, the ToF camera is inherently robust against the ambient light fluctuation that is commonly encountered in a moving vehicle. Moreover, in this study, the depth information is first used to compensate for the motion-induced artifact and then to assess the quality of the HR signal within different sections of the HR measurement (i.e., within the 20 s video). Additionally, with the depth information from the ToF camera, the CVSM system can measure RR directly from chest wall motion. We conduct a series of on-the-road testing to evaluate the performance of the CVSM system in realistic operation environments. We first show that, because of the 850 nm active illumination, the system can measure HR independent of ambient light conditions. We then show that the depth-based motion assessment/compensation greatly improves the HR measurement success rate. When the user is sitting in the parked vehicle with the engine running, the CVSM system can measure HR with 86% success rate with motion compensation and only 36.5% success rate when only the 2D grayscale information is used. Such improvement becomes more evident in more complex driving scenarios. When measuring the passenger while driving on the highway and local roads, the success rate is 82% (34% without compensation) and 66% (22% without compensation), respectively. Ultimately, in the case of measuring users driving the vehicle, the compensated HR measurements achieve a success rate of 71.9% and 56% on highways and local roads, respectvely, as compared to just 13.6% and 12% when only the grayscale information is used. Even with motion compensation, the HR measurement on the local road remains the most challenging task since both the road unevenness and the driver's active motion are  
Figure 25: Respiration motion on chest measured by the ToF camera while the participants were wearing different types of clothing. Clear respiration patterns can be seen in all cases. more significant in this case. Moreover, we show RR measurement on highway driving scenarios with an average deviation of \(-1.4\) BPM. In the end, we explore the limitations as well as potential improvements to the CVSM system. The limitations of the HR measurements using the CVSM system could come from effects such as facial expression, sudden motion or even reflection glares from the driver's eye. These limitations could potentially be alleviated using a higher frame rate ToF camera or using other post-processing algorithms to further reject these intensity artifacts. As for the RR measurement, even though the 850 nm illumination does not penetrate as deep as a millimeter-wave radar, our CVSM system is still able to measure the respiration pattern of the chest wall when the user is wearing various types of clothing. With its robustness against both ambient light and motion artifacts, the ToF-based CVSM system could be a key enabler in low-cost and compact driver/occupant monitoring systems in the vehicle. The physiological information measured by the system could one day empower a safer and more functional ADAS system. Conceptualization, K.G., M.N.I., C.D. and T.Z. ; methodology, K.G., T.Z. and E.P. ; software, K.G., M.H.P., A.V. and S.M. ; validation, K.G. and E.P. ; data analysis, K.G. and E.P. ; data curation, K.G., E.P., M.H.P. and A.D.; writing--original draft preparation, K.G. ; writing--review and editing, K.G., E.P. and M.N.I. ; visualization, K.G. ; supervision, M.N.I; project administration, M.N.I. ; funding acquisition, M.N.I. All authors have read and agreed to the published version of the manuscript. This study received partial funding from Omni Sciences.