# Emotional Health Detection in HAR: New Approach Using Ensemble SNN
## 6 Results and Discussion  
In the experiments, we used a result validation approach through the control network. In Table 3 we reported the results for the six emotions analyzed. Success rates were achieved using the original test dataset and a training dataset reduced by several images to make it more consistent. The percentages refer to each type of image (emotion) in the case of the control neural network, to the single AI NN units, and to the entire model in the Ensemble algorithm. From the analysis of the data, there is a low value for the \"Disgust\" emotion for all types of networks used, both due to the low number of samples present in the datasets and the difficulty of its identification due to the lack of expressive separability from the \"Anger\" class. We also found that network performance improved by moving from the single control network trained for the six types of emotions to those of individual networks built to identify a specific emotion (no false positives were detected). Moreover, finally adding the rules of decision and unification of the ensemble layer, we noticed that the performance of the network improved further. The success rates are almost all around 80%, with a peak of 95% for the \"Fear\" emotion. We compared the model's performance with the other proposals in the related work (Table 4). Depending on the specific functionalities of the emotion recognition system, researchers used different methodologies, technologies and databases. In our analysis, we represented different experiences testifying to a varied scenario. The results obtained show that the algorithm adopted ensures that facial emotion recognition results are compatible from the point of view of accuracy with the state-of-the-art. The efficiency of the model was based on the type of network used. SNN networks allowed us to create a model with a reduced number of levels compared to existing models. This choice arose from some considerations related to the intrinsic quality of the model (e.g., not requiring any normalization of the input data) and the peculiarities of the dataset used in conjunction with the learning phase. The dataset is, in fact, not very homogeneous in terms of the numerical distribution of classes. Therefore, two of the qualities of SELUs are very valuable: it does not have a vanishing gradient problem, and neurons cannot die, as can happen with RELUs. Then, the outputs of the individual networks are sent to the Ensemble layer, which has the task of improving the performance of the individual classification systems through the analysis of the results rendered. We achieved 98.4% accuracy with a learning rate of 0.0001 and 60 epochs, while in the test phase we obtained an accuracy value of 85%, excluding the emotion disgust. \begin{table}
\begin{tabular}{l c c c} \hline  & **Control Network** & **Sub Network** & **Ensemble** \\ \hline L0 (Anger) & 22\% & 62\% & 78\% \\ \hline L1 (Disgust) & 10\% & 18\% & 22\% \\ \hline L2 (Fear) & 82\% & 94\% & 95\% \\ \hline L3 (Happiness) & 43\% & 76\% & 81\% \\ \hline L4 (Sad) & 64\% & 83\% & 85\% \\ \hline L5 (surprise) & 54\% & 77\% & 80\% \\ \hline \end{tabular}
\end{table} 
Table 3: Success Rate. The emotion collection was carried out based on static images. The usefulness of a video would provide spatiotemporal information for expression dynamics captured in a video sequence [51]. The temporal information is accurate, allowing us to perform better. However, it involves significant differences in the characteristics extracted during the duration of the transition and in the specific characteristics of the expressions depending on the subject's physiognomy. Possible approaches to solving this type of problem are costly in terms of computational time and complexity. Therefore, they do not efficiently reduce time redundancies in extracted frames. The dataset used, which is complex in itself, presents for some classes a limited number of samples. Numerous images are not sufficiently clear, and can lead to misinterpretation.