## 3 Multimodal Emotion Analysis System
### Other Emotion Recognition Channels  
Work on the remaining two channels in the multimodal emotion recognition system has already been done in earlier stages. The most popular algorithm is emotion recognition based on facial images, where the linguistic context does not have such an impact as in the case of analyzing emotions in speech. The human face is also one of the sources from which it is easiest to read the expressed emotion. Many solutions for analyzing human facial expressions are based on datasets categorized, as in this case, according to basic emotions defined by [PERSON]. The datasets worth mentioning are the FER2013 dataset (Facial Expression Recognition 2013 Dataset) [17] and its extension FER+ [18], in which the photos were captured spontaneously. The third dataset is the CK+ (extended Cohn-Kanade dataset) [19], which is a video set of simulated transition behavior from a neutral state to expressing the target emotion. Based on these datasets, many solutions for image classification algorithms were created, mainly based on ResNet (residual network)-type neural networks. ResNet is a deep learning model designed to solve the problem of vanishing gradients in very deep neural networks by using the so-called \"skip connections\" or residual blocks, which allows training much deeper networks with better accuracy. Example solutions from recent years indicate the accuracy of models at the level of 80-90% [20; 21; 22], which in the case of recognizing a subjective thing such as emotion is a very good result. Additionally, in the case of the vision system, work was also carried out on algorithms for detecting an active speaker, which in the case of a multimodal system will allow for the separation of people whose emotional state is analyzed from potential interlocutors or people appearing in the background [23].  
The third information channel through which emotions are transmitted is biomedical data-physiological signals of the human body. We can distinguish the following biomedical signals: EDA (electrodermal activity), BVP (blood volume pulse), EEG (electroencephalography), EMG (electromyography), RSP (respiration), SKT (skin temperature), and HRV (heart rate variability). In their work, datasets concerning these signals were compared, but under two conditions, which are conditioned by the multimodal system using the Aidmed One device and excluding equipment and signal studies that require clinical/medical conditions (the system is to be user-friendly). On this basis, the YAAD (Young Adult's Affective Data) [24] dataset was selected and classic classification models such as decision trees, random forest, support vector machine, k-nearest neighbor, extreme gradient boosting, linear regression, and voting classifier were tested. The accuracy of these models oscillated around 60%, which gave satisfactory results for sets that did not have a very large sample of data (most of the datasets are signals collected from a group of several dozen people). In the future, it is planned to use neural networks for this algorithm, but the priority will be to create a dataset thanks to the multimodal emotion analysis system.