## 5 Discussion  
In the previous sections, we have shown the benefits of using both the active illumination and depth information from an indirect ToF camera for HR and RR measurement in a vehicle. However, because of the complex environments in a vehicle cabin, it is worth discussing the potential limitations of the current methods and exploring potential approaches that could be used to improve the robustness and accuracy of our CVSM system. **Potential Limitations and Improvements for HR measurements:**  
To obtain HR reliably, the system needs to keep track of the same ROI accurately across all frames. Facial expression or facial occlusions could all interfere with the acquisition of ROIs on the face. If the vertices of the ROI are jittering from frame to frame, the system could mistake the ROI jittering frequency as the HR. For example, Figure 21 shows an example where the blinking of the participant interferes with the HR measurement. The participant is asked to blink his eye at the same frequency of an external metronome at 115 BPM (selected to be out of the normal range of the participant's resting HR). The periodic bright reflection from the eye causes the ROI-1 locations to jitter at the same frequency, which introduces an artifact frequency that overwhelms the true HR frequency component. Since this type of error rises from the jittering of ROI locations, they could potentially be mitigated by using a face tracking algorithm that is less sensitive to external interference. Furthermore, one possible solution is to check the grayscale intensity change against the position change of the ROIs to make sure the dominating frequency peak in the frequency domain is not from the jittering of ROIs [11]. In addition to the interference of the ROI acquisition, subtle motions such as facial expression change and talking could also limit the performance of the CVSM system because these motions could introduce motion artifacts without causing detectable depth changes from the ROI [28]. These subtle artifacts that are confined to localized areas on the face could be alleviated with methods such as the sparse frequency estimation and will be discussed in the later sections. Moreover, a unique challenge to monitoring HR in a vehicle is the sudden motions from either the driver/passenger or the road conditions. In order to capture these motions accurately, it might be beneficial to use a camera with a higher frame rate. To investigate the potential benefits of using a higher frame rate camera, we measured HR on three participants while driving on the highway at 60 FPS (compared with 30 FPS in the experiment section). The acquired 60 FPS videos are then down-sampled (only using every other frame in the original video) to 30 and 15 FPS to compare the performance of HR measurements at different frame rates. Each frame in the 60/30/15 FPS video shares the same integration the time and, therefore, the same background noise. Figure 22 shows the error rate distribution for the HR measurements on the highway. With a higher frame rate (60 FPS), the motion across two consecutive frames is smaller, which makes tracking the  
Figure 21: Periodic reflection from participant blinking could affect the location of the ROI-1 as a result of jitter. The jittering can lead to additional intensity artifacts that will not be captured by the (**a**) depth signal, leading to erroneous HR reading from the (**b**) frequency domain. ROI easier when sudden or large motions are present. In our preliminary test, the original 60 FPS videos result in a success rate of 76% while the down-sampled 30 FPS videos leads to a reduced success rate of 68%. When the frame rate is further reduced to 15 FPS, a further degradation of the success rate to 58.7% is observed. The given benefits of a higher frame rate could be attributed to the smaller motion across two consecutive frames.