## 5 Conclusions  
In conclusion, there has been a notable increase in the volume of research studies focusing on unimodal approaches, specifically those related to facial expression analysis, text sentiment analysis, and dialogue emotion recognition. This surge in research activity can be attributed to significant advancements in information processing techniques, natural language understanding, and the growing accessibility to online data sources.  
In the realm of physiological signals, a noteworthy trend has been the rising interest in studies centered around EEG signals, which have been extensively explored and have demonstrated exceptional results in emotion recognition tasks. However, such studies often require medical-grade or intrusive devices. Although there have been advancements in creating more compact versions of these devices, they still cannot match the convenience and non-intrusiveness of, for example, a smartwatch, which is already a part of everyday life for many people. Therefore, the development of technologies that can utilize non-intrusive devices to achieve similar levels of effectiveness as EEG-based systems remains a significant challenge. Additionally, the modality of fusing multi-physiological and physical signals could be key to addressing this problem. This approach could potentially enhance the accuracy and practicality of emotion recognition systems, making them more suitable for everyday use. This underscores the importance of continuing to explore and develop diverse modalities within emotion recognition research. However, the limitation for this development lies in different complexities; these signals vary significantly between individuals, making it difficult to standardize them for broad-spectrum applications. The processing and analysis of physiological data is complex and requires the development of advanced algorithms to correctly interpret the signals. In addition, building a reliable and representative database requires a large sample of participants to capture interpersonal variability. Due to these complexities inherent in the use of physiological signals in affective computing, there is still a wide area of opportunity for the development of studies in this field. These challenges not only highlight the need for innovative approaches and more advanced technological solutions, but also underscore the importance of interdisciplinary research spanning areas such as psychology, computer science, and bioengineering. These solutions could lead to significant advances in how machines understand and respond to human emotional states, expanding the possibilities for their application in a variety of fields.  
Conceptualization, R.A.G.-H.; methodology, R.A.G.-H. and H.L.-G.; validation, R.A.G.-H., H.L.-G. and J.M.C.-P.; formal analysis, R.A.G.-H.; investigation, R.A.G.-H.; resources, A.G.-H., L.C.R.-G., [PERSON]-C., [PERSON]-C., D.R. and [PERSON]-C.; data curation, [PERSON]-H.; writing--original draft preparation, [PERSON]-H.; writing--review and editing, [PERSON]-H.; visualization, [PERSON]-H., [PERSON] and [PERSON]-P.; supervision, [PERSON]-G., [PERSON]-P. and [PERSON]-C.; project administration, [PERSON]-H., [PERSON]-G. and [PERSON]-P.; funding acquisition, K.O.V.-C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors thank CONACYT for the support granted by their national scholarship program, CVU number 307551, to [PERSON].  
The authors declare no conflicts of interest.