# Emotional Health Detection in HAR: New Approach Using Ensemble SNN
## 7 Conclusions  
In this article, we have developed a facial expression recognition system that can help improve healthcare. Despite the results obtained with technological progress in the development of automatic emotion recognition systems, this technology, as observed from the review of the literature, is not widely used in the health system. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Authors** & **Purpose** & \multicolumn{2}{c}{**Technologies**} & **Database** & **Efficiency** \\ \hline \multirow{3}{*}{[PERSON] et al. [12]} & \multirow{3}{*}{Direct diagnosis of disease} & \(\bullet\) & Deep Transfer & \multirow{3}{*}{VGG-Face} & \multirow{3}{*}{93\% accuracy} \\  & & \(\bullet\) & Learning DTL & & \\  & & \(\bullet\) & MatConvNet & & \\ \hline \multirow{3}{*}{[PERSON] et al. [13]} & \multirow{3}{*}{Improved facial emotion recognition using pseudo RGB-D} & \(\bullet\) & RGB-D sensor & \(\bullet\) & Bosphorous 3d & \multirow{3}{*}{97\% SSIM (Similarity} \\  & & \(\bullet\) & Depth plus & \(\bullet\) & Face & \\  & & \(\bullet\) & Generator & \(\bullet\) & CASUA 3d Face & \\  & & \(\bullet\) & Adversarial & \(\bullet\) & Bu-3 DFe & image quality) \\  & & & network & & \\ \hline \multirow{3}{*}{[PERSON] et al. [14]} & Improved facial emotion recognition using physiological signals & \(\bullet\) & RNN & \multirow{3}{*}{ISEAR} & \multirow{3}{*}{96\% accuracy} \\  & & \(\bullet\) & HRV sensor & & \\ \hline \multirow{3}{*}{[PERSON] et al. [17]} & Improved facial emotion recognition using physiological signals & \(\bullet\) & t-SNE & \multirow{3}{*}{SEED dataset} & 0.625 \(\pm\) 0.029 CCC \\  & & \(\bullet\) & PCA & & (Conordance \\  & & \(\bullet\) & SVR & & correlation coefficient) \\  & & \(\bullet\) & PSD & & \\ \hline \multirow{3}{*}{[PERSON] et al. [19]} & Improved facial emotion recognition using MEMS, environmental an physiological signals in real-life environments & \(\bullet\) & CNN & \multirow{3}{*}{ENvBodySens} & \multirow{3}{*}{95\% accuracy} \\ \cline{1-1} \cline{5-5}  & Pain or discomfort recognition in patients monitoring & \(\bullet\) & Haar classifier & & \\ \hline \multirow{3}{*}{[PERSON] et al.} & \multirow{3}{*}{Face emotion recognition} & Ensemble SNN & Kaggle & \multirow{3}{*}{98.4\% accuracy in training and 85\% in test} \\  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of face emotion recognition systems. The solution we propose is based on the Ensemble AI model. The methodology applied is part of an ensemble learning area in which the models, in comparison, discriminate two classes, with one referring to the specific emotion for the network for which it was trained and the other referring to \"other emotion\" class. The advantages obtained were:  
\"Almost observable states\". It is possible to investigate and highlight which module caused errors. It operates a debugging similar to the case of deterministic algorithms. \"Modularity and parallelism\". Individual modules can be trained on different work-stations, at different times, without synchronization between parts. This feature allows one to independently develop different configurations and calibrations of the specific module research groups. \"Embedded application\". The Vectorflow framework in D language allows the realization of the model even in embedded hardware on many platforms and operating systems. The results show an increase in performance compared to the control neural network, confirming that the proposed system can recognize emotions with high precision. With this system, doctors and healthcare professionals can constantly monitor, as part of the broader human activity recognition system, the psychophysical conditions of patients, detecting malaise, pain and fatigue and taking appropriate actions as needed. This solution can be seen as a component of smart healthcare centers. In the future, we plan to extend the work by investigating the infrastructure on a less complex dataset and analyzing video sequences. [PERSON] and [PERSON] contributed to conception and design of study.