## 4 Conclusions and Outlook  
In this work, a novel sensor-based methodology was proposed to assess the circadian effectiveness of the prevalent lighting conditions caused by artificial and daylight light sources using simple and inexpensive RGB color sensors that can easily be integrated into modern lighting systems for proper monitoring and advanced lighting control. It was shown that the sensor readouts, after some suitable processing, can be used to estimate CS, a measure of circadian effectiveness, with high accuracy. For artificial light sources, the \(RGB\) sensor responses were mapped to CIE \(XYZ\) tristimulus values first by applying a suitable 3 transformation matrix. From these \(XYZ\) values, the corresponding chromaticity coordinate \(z\) can be calculated. Together with the lighting condition's illuminance \(E_{\
u}\), this provides the input for the application of [PERSON] et al. 's approximation formula, which yields an estimate for the circadian effectiveness in terms of CS. Compared to [PERSON] et al. 's original model description, only small model prediction errors (typically less than 0.028 \(\Delta CS\)) were observed for a broad variety of artificial test light sources, emphasizing the suitability of the proposed approach with regard to predicting sufficiently accurate CS values from \(RGB\) sensor output for light sources usually found in the indoor lighting context. For daylight light sources, on the other hand, an estimation of their spectral compositions from \(RGB\) sensor readouts was performed first by adopting the CIE daylight model. After converting the color sensor's \(RGB\) output to the CIE 1960 chromaticity space by applying a daylight-specific 3 \(\times\) 3 matrix transform and subsequent linear transformation, \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Sampling Time** & **07:27** & **10:03** & **11:06** & **12:03** & **13:05** & **14:07** & **15:10** & **16:12** & **19:14** \\ \hline \multicolumn{8}{c}{CS\({}_{2018}\) and other parameters directly calculated from the measured daylight spectra} \\ \hline CCT in K & 12,464 & 8033 & 6313 & 5651 & 5914 & 5853 & 5470 & 8174 & 16,066 \\ \(E_{\
u}\) in lux & 401 & 9397 & 23,940 & 58,212 & 39,463 & 43,150 & 71,006 & 15,058 & 4613 \\ \(x_{\text{meas.}}\) & 0.2652 & 0.2942 & 0.3163 & 0.3291 & 0.3236 & 0.3248 & 0.3332 & 0.2919 & 0.2568 \\ \(y_{\text{meas.}}\) & 0.2831 & 0.3066 & 0.3293 & 0.3413 & 0.3364 & 0.3376 & 0.3449 & 0.3081 & 0.2705 \\ \(\text{CS}_{2018,\text{origin}}\) & 0.528 & 0.693 & 0.697 & 0.699 & 0.698 & 0.699 & 0.699 & 0.696 & 0.561 \\ \hline \multicolumn{8}{c}{CS\({}_{2018}\) and other parameters estimated from the color sensor readouts by applying the CIE daylight model} \\ \hline CCT\({}_{\text{reconstr.}}\) in K & 12,561 & 8044 & 6306 & 5647 & 5907 & 5845 & 5470 & 8146 & 16,309 \\ \(E_{\
u,\text{processed}}\) in lx & 402 & 9389 & 24,051 & 58,213 & 39,593 & 43,239 & 70,871 & 15,180 & 454 \\ \(\Delta u^{\prime}v^{\prime}\cdot 10^{3}\) & 4.6 & 1.8 & 2.0 & 2.0 & 1.9 & 1.9 & 2.0 & 0.57 & 3.7 \\ \((Meas.-Reconstr.)\) & & & & & & & & & \\ \(\text{CS}_{2018,\text{reconstr.}}\) & 0.530 & 0.693 & 0.697 & 0.699 & 0.698 & 0.699 & 0.699 & 0.696 & 0.559 \\ \(|\Delta\text{CS}_{2018}|\) & 0.002 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.001 \\ \((Meas.-Reconstr.)\) & & & & & & & & & \\ \hline \multicolumn{8}{c}{CS\({}_{2018}\) and other parameters estimated from the color sensor readouts by applying [PERSON] et al.’s model} \\ \hline \(\Delta u^{\prime}v^{\prime}\cdot 10^{3}\)\({}_{(Meas.-Calc.)}\) & 0.47 & 0.0 & 0.174 & 0.12 & 0.182 & 0.196 & 0.0596 & 0.403 & 0.85 \\ \(\text{CS}_{2018,\text{Truong}}\) & 0.517 & 0.691 & 0.696 & 0.698 & 0.698 & 0.698 & 0.699 & 0.695 & 0.548 \\ \(|\Delta\text{CS}_{2018}|\)\({}_{(Meas.-Calc.)}\) & 0.011 & 0.002 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.013 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison between the color sensor’s CS predictions (\(\text{CS}_{2018,\text{reconstr.