## 5 Discussion
Therefore, even though the sparse frequency estimation method correctly identified the HR at 62 BPM, it also included the artifacts that are common to the three ROIs in the extracted frequencies (Figure 23b). One potential solution to this challenge is to also utilize the fact that HR is usually slowly varying in time. One could potentially find the sparse frequency that is common across both multi regions on the face as well as within several consecutive HR measurements in time. By combining both time and multiple ROIs on the face, such method could hopefully further reject noises from the compensated HR signal and improve the signal reliability. Finally, in this study, the open-source face landmark identification and tracking algorithms [35] are trained mostly with RBG images in various environments and, therefore, may not be optimized for videos that are taken in the NIR spectrum. In the future, commercially available face tracking algorithms (i.e., Visage, Seeing Machine, Emotion3D, etc.) specifically designed for facial landmark tracking in NIR videos in driving environments might be used to improve the robustness against sudden motion or reflection glares. With the help of a more optimized face tracking algorithm, one could alleviate these problems mentioned in the previous discussion, which in turn improve the reliability and accuracy of the HR measurements in the vehicle. **Potential Limitations and Improvements for RR measurements:**  
Regarding using a ToF camera for RR measurements, a natural question to ask is how would different types of clothing (that could block or dampen the motion of the chest wall) affect the performance of RR measurement using the ToF camera. Even though the ToF  
Figure 23: (**a**) Grayscale signal from three different ROIs on the face captured with the VCSEL illuminated ToF camera, showing similar patterns of artifacts among all three ROIs. (**b**) Extracted sparse frequency component using the method in [11], showing both HR and artifact frequency components. camera offers the capability of directly measuring RR similar to a millimeter-wave radar, one drawback is that the 850 nm light cannot penetrate clothes compared to the millimeter wave used in radar. To test the effect of clothing, we measure the depth change on ROI-2 while the participants wear clothes of different degrees of thickness (Figure 24). Fortunately, in all cases, even including the case of a thick down jacket, we were able to detect the chest wall motion from respiration in various commonly worn clothes (Figure 25). Only when the participant is purposefully wearing multi-layer \"puffy\" clothes with air gaps in between did we start to see the errors of RR measurements increasing, as the puffy clothing can block the chest motion from the camera (Table 4). Finally, it should still be noted that the typical amplitude of the chest motion is only \(\pm\)2 mm, and the RR is derived solely from the depth data. In the existence of large external motion, it is still possible for the excessive motion to corrupt the RR measurements. Such limitation could be mitigated by adding complementary RR measurements methods such as RR extraction from HR variability or through extra sensors such as thermal cameras that are co-equipped in the vehicle [20; 21; 29]. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Clothing** & **T-Shirt** & **Sweater** & **Rain** & **Wool** & **Down** & **Multi-Layer *** \\
**Type** & & & **Jacket** & **Coat** & **Jacket** & **Clothing** \\ \hline Absolute Mean & 1.77 & 1.56 & 1.51 & 1.02 & 1.75 & 3.57 \\ Error (BPM) & & & & & & \\ \hline \end{tabular}  
* “Multi-layer” clothing refers to wearing several different clothes with air gaps in between.  
\end{table} 
Table 4: Absolute mean error of RR measurement when the participant is wearing different types of clothing. Figure 24: Types of clothes worn by participants in this study. Participants were asked to sit in a parked car with the engine running and breathe naturally during the measurements.