## 4 Experiment Results  
The raw and processed data from the two ROIs are illustrated in Figures 8 and 9. Figure 8 shows a 30 s segment of a typical 60 s recording of both the raw intensity and depth signal for ROI-1. The second row corresponds to the raw intensity information and the bottom row corresponds to the depth information from the same ROI-1. In ROI-1, the maximum motion from ROI-1 to the camera is as large as 15 cm. As a result, the raw intensity data is strongly inversely correlated to the depth information from the same ROI, suggesting that the raw signal is dominated by the motion artifacts. The grayscale intensity after motion compensation is shown on the top row of Figure 8 where most of the motion artifacts in the depth information are removed. From the time-domain signal, the heart rate signal becomes recognizable compared to the motion-dominated raw signal. The effect \begin{table}
\begin{tabular}{l l l l l l l} \hline Group: & \multicolumn{2}{l}{Number of Participants} & \multicolumn{2}{l}{Age Range} & \multicolumn{2}{l}{Skin Type Range} \\  & 29 & & 18–72 years & & II–IV \\ \hline Subgroup: & (Female & Male) & (\(<=35\) years & \(>\)35 years) & (I–III & IV–VI) \\  & 11 & 18 & 21 & 8 & 17 & 12 \\ \hline \end{tabular}
\end{table} 
Table 3: Demographics of the participant population. Figure 7: RR extraction process from the 3D video streams of a ToF camera. of the motion artifacts as well as the benefits of depth-based motion compensation is also evident in the frequency domain. When comparing the frequency domain signal of the raw intensity and the depth signal, we can see the two spectra are similar to each other in shape, and they also share many common lower frequency components. Even though a weak frequency peak near the reference HR of 83 BPM can be seen, it is overshadowed by the frequency component from the motion artifacts. After the motion compensation, we can see that in the frequency domain signal of the compensated signal (Figure 8b, top), these artifact frequencies are suppressed and a clear HR frequency near 83 BPM can be seen in the compensated spectrum. As for the respiration rate measurement, Figure 9 shows a typical depth signal from the ROI-2 (chest) region corresponding to a RR of 9 BPM. The amplitude of chest wall displacement caused by the respiration motion is approximately 4 mm. Because of this small amplitude, such motion could be affected by other factors such as overall body motion as well the blockage of clothing. Some potential solutions to mitigate these issues will be discussed in the discussion section. The pilot human study with 29 participants illustrates the improvement in overall performance achieved for the HR measurements by using depth information compensation. In the study, the mean HR reading during the 60 s of measurement is considered as the ground truth and the \"correct measurement\" from our system is defined as a HR measurement that is within 10 percent of the ground truth. The top bar graph in Figure 10 demonstrates the distribution of measurement error rate after compensation, while the bottom bar graph demonstrates the distribution of measurement error rate without depth compensation. With depth information used to compensate for motion artifacts, 75% of the HR measurements (214 out of 286 measurements) are correct. On the other hand, without compensation, only 35% of the HR measurements (100 out of 286 tests) are correct. It is also worth noticing that with motion compensation, many of the erroneous measurements are below 30% error rate, as compared to between 50 and 60% for the uncompensated case. Figure 8: (**a**) Time domain signal for HR extraction: HR signal after motion compensation (top), raw grayscale signal (middle) and raw depth signal (bottom). (**b**) Frequency domain HR signal: with motion-compensation (top), without motion compensation (bottom). Figure 10: HR measurements error rate on all 286 measurements with motion compensation (**top**) and without motion compensation (**bottom**).