## 3 Different Aspects of Colour-Based Method
### Both Illumination Variations and Motion Artefacts  
Most studies are concerned with either motion artefacts or illumination variations. Only a few studies have considered both illumination variations and motion artefacts as issues. Table 4 summarizes the studies concerned with both illumination and motion. [PERSON] et al. [95] introduced a normalized least mean square (NLMS) adaptive filtering scheme to suppress the effect of both the subject's motion and illumination variations considering realistic situations, for example, watching movies or playing games by using an iSight camera of an IPAD to measure cardiac pulse. Nevertheless, this method was seriously affected by large head movements. Using a monochrome camera, [PERSON] et al. [96] designed a robust method to extract HR and HRV based on a weighted average and considering different skin tones, illumination variation, and several motion scenarios. [PERSON] et al. [97] presented a new noise elimination technique using both complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and canonical correlation analysis (CCA) to eliminate noise artefacts caused by movement of subjects and camera, variations of illumination and variation of skin tone. As shown in Figure 7, firstly, data was collected using a UAV from a 3 m distance at various times of day with various illumination levels. Then, an improved video magnification technique [98] was used to magnify skin colour variation. After that, an efficient face detection method [99] was used to detect facial ROI as it is more effective with an inclined or angled face. Raw iPPG signal was extracted using spatial averaging within the facial ROI of the green channel. To suppress the noise caused by illumination variations, CEEMDAN was applied as it showed better performance than EMD and EEMD by decreasing noise from the intrinsic mode functions (IMFs) with more physical meaning. BY using CEEMDAN, the iPPG signal was decomposed into eight IMFs and the 5 th, 6 th and 7 th IMFs were selected as their frequency bands fall within 0.2-4 Hz, corresponding to 12 to 240 beats/min. Then, to reduce motion artefacts, CCA was used on chosen IMFs as CCA generates components derived from their uncorrelated signals rather than independence components used in ICA and gives better performance than ICA. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Used Sensors with Parameter** & **Vital Signs** & **ROIs** & **Used Technique** & **Distance (m)** & \begin{tabular}{c} **No. of Participants** \\ **at a Time** \\ \end{tabular} & **Results** \\ \hline \multirow{2}{*}{[PERSON] et al. [95]} & iSight camera of an & & & NLMS & & \\  & IPAD resolution & HR & Face & \
\begin{tabular}{c} adaptive \\ filtering \\ \end{tabular} & 0.35–0.50 m & 1 & RMSE = 1.8 bpm \\ \hline \multirow{2}{*}{[PERSON] et al. [96]} & Monochrome camera & HR and & & & & \\  & 30 fps & HRV & Face &
\begin{tabular}{c} Weighted \\ average \\ \end{tabular} & 0.5 m & 1 & SNR (dB) = 6.5 \\ \hline \multirow{2}{*}{[PERSON] et al. [97]} & Hovering UAV & & & & & \\  & resolution 1920 \(\times\) 1080, & & & & & \\  & 60 fps & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: iPPG based methods concern about both illumination variations and motion artefacts.