## 5 Discussion
Although previous research showed that participants took the experimental task seriously, FaceReader identified their emotions as anger [83]. These results prove that FaceReader can detect subtle and instantaneous changes in facial expressions in an objective manner and draw conclusions with a high degree of accuracy using potentially representative emotion components as a basis. Observations of the researchers and verbal responses of the participants are, however, necessary to understand the way participants feel and to discuss the face recognition results further. The key point of the facial recognition experiment is to distinguish changes in emotions. Each emotion is of a very short duration of about 0.5-4 s [66], thus differences between data points are difficult to quantify and not every frame of a participant's video may be analysed. FaceReader can be used for reliable analysis of emotions by studying the way facial expressions of participants change, but researcher observation, participant self-reports and proper experimentation are also needed to minimise the potential for inconsistent results. The correlation signs shown in Table 13 are consistent with studies conducted around the world. Many studies, for instance, show that an increase in happiness leads to decreases in sadness (e.g., [84; 85]). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Correlations** & \multicolumn{2}{c}{**Data from Our Experiments**[38; 39; 40; 41; 42; 43; 44; 45]} & \multicolumn{4}{c}{**Data by Other Authors**} \\ \hline  & Happiness level measured in case study & Heart rate measured in case study & Sadness level measured in case study & Happiness level measured by [PERSON] et al. [86] & Heart rate measured by [PERSON] et al. [87] & Sadness level measured by [PERSON] et al. [84] \\ \hline Happiness level measured in case study & \(-\) & 0.6055 & \(-\)0.7826 & 0.6464 & 0.6908 & \(-\)0.7431 \\ \hline Heart rate measured in case study & \(-\) & \(-\)0.2411 & 0.4182 & 0.7203 & \(-\)0.3408 \\ \hline Sadness level measured in case study & & \(-\) & \(-\)0.8201 & 0.2967 & 0.7791 \\ \hline Happiness level measured by [PERSON] et al. [86] & & & \(-\) & \(-\)0.1149 & \(-\)0.6121 \\ \hline Heart rate measured by [PERSON] et al. [87] & & & & \(-\) & 0.2801 \\ \hline Sadness level measured by [PERSON] et al. [86] & & & & & \(-\) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Correlations between the level of happiness, sadness and HR obtained in this experiment and the data of other researchers. According to [PERSON] et al. [88], various broad models can be used to express emotions. Happiness, sadness, wrath, surprise, contempt and fear are among the main emotions, according to some authors [45]. According to some researchers [89], this model has shortcomings when it comes to accurately representing particular emotions in a wider spectrum of affective states. Measurement, representation and modelling of emotion recognition strongly rely on psychological theories for their guidance [90]. However, the concept of emotion theory is still developing and subject to vigorous discussion [91; 92]. Additionally, context [93] can attenuate the effects of cultural, geographic and individual differences on how we may perceive, experience and express emotions [94; 95]. This study has some limitations. The following factors need to be taken into account in any upcoming research:  
* Planning specialists, architects, constructors, etc. do not always have access to reliable, depersonalised, real-time biometric data, which restricts the method's use. Due to this, gathering data is both prohibitively expensive and time-consuming. * Renovation projects could be more accurate by taking into account social, hedonic and customer-perceived values, emotional, affective, and biometric maps, in addition to those of the surrounding environment. As a result, more useful advice for enhancing renovation process efficiency would be generated. * Data and customer privacy concerns are the main obstacle to using the ASP System.