# Emotional Health Detection in HAR: New Approach Using Ensemble SNN
###### Abstract
Additionally, regarding privacy, the use of facial images must comply with the laws on processing personal data. In interpreting images in the biomedical field, specific studies have been presented on improving the efficiency of deep learning. Below are some of the most significant examples. [PERSON] et al. [9] developed an interesting method of deep learning in bioimaging called VoxelEmbed. Their work was finalized in the realization of an innovative multistream approach that facilitates embedding pixels with 3D contextual information. This solution arose from the need to meet the need for tools for the analysis of the dynamics of living cells. The segmentation and tracking of cell instances based on pixel incorporation have proven helpful for studying cellular dynamics. This method was validated through tests on four 3D datasets of the Cell Tracking Challenge. Meanwhile, [PERSON] et al. [10] proposed a system to optimize the feature boundary of deep CNN through a two-step training method: a pre-training step and implicit regularization. Regularization is the process of enhancing the generalization ability of a CNN in order to train complex models while maintaining lower overfitting. It has a primary role in the tuning parameters of deep CNN. In the pre-training step, the authors trained the model to obtain the image representation for anomaly detection. Based on the anomaly detection results, the implicit regularization step re-trained the network to regularize the feature boundary and obtain the convergence. The implicit regularization can be considered as an implicit model ensemble. It can be regarded as the training process of a different network that shares weights with different training samples; it can be viewed as a combination of a wide number of similar CNNs trained with different datasets. Finally, [PERSON] et al. [11] presented interesting findings relating to overcoming the problem of training self-supervised machine learning algorithms using massive data of biomedical images from databases or specialized journals. However, the images from such sources consist of a considerably large amount of compound figures with subplots. In their work, these authors developed a framework (SimCFS) for separating compound figures using weak classification annotations. In particular, they proposed a system to separate the compound figures with lateral loss. The training stage contains these steps. SimCFS only requires single images from different categories. The pseudo compound figures are generated from the augmentation simulator (SimCFS-AUG). Then, a detection network (SimCFS-DET) is trained to perform compound figure separation. In the testing stage, they used only the trained SimCFS-DET for separating the images. For evaluating the performance of different compound figure separation strategies, they used one compound figure dataset (called Glomeruli-2000) consisting of 917 training and 917 testing real figure plots from the American Journal of Kidney Diseases (AJKD). The proposed method SimCFS-DET was then compared with the most used methods for separating compound figures showing better performance. ImageCLEF2016 was used as the dataset, containing 8397 figures, of which 6783 were for training and 1614 for testing. The system ultimately allows efficient distribution to new image classes. In this work, a facial emotion recognition system based on an innovative approach of an SNN ensemble network is designed to support the work of medical staff in the diagnostic evaluation of the type of detectable malaise. The work after the introduction containing the importance of FER is articulated as follows: Section 2 presents the related works, Section 3 presents the analysis of the problem, Section 4 provides the methodology, Section 5 sets out the dataset and model design, Section 6 presents the results and discussion, and finally, Section 7 provides the conclusions.