# This would involve more sophisticated calculations, but here we use a simple placeholder
## 7 Experimental Results
\begin{table}
\begin{tabular}{l l l} \hline
**Emotions** & **Absolute \%** & **Relative \%** \\ \hline Confidence & 85 & 21.25 \\ \hline Anxiety & 15 & 3.75 \\ \hline Interest & 93 & 23.25 \\ \hline Disinterest & 7 & 1.75 \\ \hline Excitement & 76 & 19.00 \\ \hline Indifference & 24 & 6.00 \\ \hline Self-esteem & 34 & 8.50 \\ \hline Frustration & 66 & 16.50 \\ \hline \end{tabular}
\end{table} 
Table 2: Absolute and relative percentages of responses to the questionnaires with respect to the eight considered emotions. Here, we give the statistical results for the emotive questions: \begin{tabular}{l c} \hline \hline Mean & 68.6\% \\ \hline Standard Deviation & 21.7\% \\ \hline Min & 27.0\% \\ \hline Max & 98.0\% \\ \hline \hline \end{tabular}   
The findings highlight the significant impact of emotional states, particularly excitement and frustration, on the museum experience. Analysis of the data revealed that the emotional states of excitement and frustration have a large bearing on visitor engagement  
Figure 4: Distribution of responses to questionnaires with respect to the different emotions. Figure 5: Experimental results for emotive intensity with respect to the 36 questions in the questionnaires. and learning outcomes. Visitors in excited states were found to have deeper engagement with exhibits, which may suggest that this emotional state increases cognitive stimulation and retention of information. Frustration, on the other hand, points to design challenges that could detract from the museum experience. This finding flags the importance of emotional computing in the design of adaptive museum experiences. By using real-time emotion recognition, museums may tailor interactions and adjust the content of exhibits according to the emotions of visitors. This ability can team up with the goals of the Future Internet towards creating digital ecosystems that are not only responsive but also emotionally adaptive, to promote positive engagement with fewer barriers to learning. To validate the emotional recognition model, we applied various statistical techniques to analyze and interpret the data gathered from the questionnaires. The data were processed using factor analysis, aimed at identifying the key emotional drivers influencing visitor engagement. Likert-scale responses (ranging from 1 to 10) were collected, allowing for quantitative analysis. The statistical reliability of the model was ensured using Cronbach's alpha, which tested the internal consistency of the emotional scale and returned a reliability coefficient of 0.82, indicating high reliability. We ensured that the assumptions of ANOVA were met by conducting preliminary tests to assess the homogeneity of variance, such as [PERSON]'s test, and by verifying that the data followed a normal distribution. When assumptions were not met, alternative non-parametric tests were considered. Regarding the cross-validation of the emotional recognition model, a k-fold cross-validation approach was adopted. This method allowed us to test the robustness of the model and optimize its parameters; it is particularly useful in multimodal contexts where signals from various sources (such as facial expressions and posture data manually annotated by the human operator) are integrated. Cross-validation helped prevent overfitting and ensured that the model was generalizable to new data. To assess the statistical significance of the emotional recognition model, ANOVA (Analysis of Variance) was employed. The ANOVA results showed that emotional states such as excitement, frustration, and curiosity had a statistically significant effect on visitor engagement (\(p<0.05\)). For further validation, cross-validation was performed on the dataset. The dataset was divided into training and test sets, with the model achieving an accuracy of 85% in classifying emotional states correctly. This approach ensures that the findings are generalizable and that the model is robust across different datasets.