## 3 Results
### Multi-Physiological Data Approaches  
In Figure 6, the \(x\)-axis displays the signal modalities present in articles classified as multi-physiological, indicating the use of more than one type of physiological signal data in their analysis. On the \(y\)-axis, the chart represents the number of articles for each different modality. This category comprises 21 articles, out of which 9 utilize their own data for analysis. The remaining studies make use of the 6 databases described in Table 7, known as benchmark databases for affective computing using physiological signals. Some of the references next to the database name indicate one of the studies in which this database was used, or the actual study in which the database was made public. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Database Name** & **Description** & **Advantages** & **Limitation** \\ \hline SAMM (Spontaneous Micro-expression Dataset) [60] & Focuses on spontaneous micro-expression that last only a fraction of a second. It contains videos of people expressing emotions in real emotional situations. Focus on the detection of micro-expressions in response to emotional stimuli. They contain videos of micro-expressions. & Real spontaneous micro-expressions. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Database Name** & **Description** & **Advantages** & **Limitation** \\ \hline WESAD (Wearable Stress and Affect Detection) [62] & It focuses on stress and affect recognition from physiological signals like ECG, EMG, and EDA, as well as motion signals from accelerometers. Data were collected while participants performed tasks and experienced emotions in a controlled laboratory setting, wearing wearable sensors. & Facilitates the development of wearable emotion recognition systems. & The dataset is relatively small, and participant diversity may be limited. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Databases utilized in the studies classified as multi-physiological and multi-physical–physiological in this review. It is important to note that some of these databases also include audiovisual data, which is why they are also mentioned in some of the multi-physical-physiological modality analysis. As a result of the analyzing articles classified as multi-physiological, which use fusions of physiological signals, several important points can be highlighted:  
* The fusion of physiological signals, such as EEG, ECG, PPG, GSR, EMG, BVP, EOG, respiration, temperature, and movement signals, is a predominant trend in these studies. The combination of multiple physiological signals allows for a richer representation of emotions. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Database Name** & **Description** & **Advantages** & **Limitation** \\ \hline MAHNOB-HCI (Multimodal Human Computer Interaction Database for Affect Analysis and Recognition) & Includes multimodal data, such as audio, video, physiological, ECG, EDA, and kinematic data. & Offers a rich dataset for studying emotional responses during interactions with technology. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
Figure 6: Quantity of articles classified as multi-physiological published from 2018 to 2023. * Most studies apply deep learning models, such as CNNs, RNNs, and autoencoder neural networks (AE), for the processing and analysis of these signals. Supervised and unsupervised learning approaches are also used. * These studies focus on a variety of applications, such as emotion recognition in healthcare environments, brain-computer interfaces for music, emotion detection in interactive virtual environments, stress assessment in mobility environments for visually impaired people, among others. This indicates that emotion recognition based on physiological signals has applications in healthcare, technology, and beyond.