# The Novel EfficientNet Architecture-Based System and Algorithm to Predict Complex Human Emotions
## 4 Result and Discussion  
As shown in Figure 8, when we trained the model with only image data, we achieved 74% accuracy, and when combining bio-signals, we confirmed 88.2% accuracy. This suggests that accuracy increases when facial expression images and bio-signal data are analyzed together. Accordingly, we developed an algorithm that predicts emotional states using a CNN for deep learning to analyze bio-signals and facial expression images together. We used EfficientNet-B0, a lightweight and high-performance model with a small number of parameters, to implement an emotion prediction model. In the FER-2013 dataset, our image-based model achieved a classification accuracy of 74%, showing better results than several existing models. Specifically, [PERSON] et al. (2018) reported an accuracy of 65.3%; [PERSON]  
Figure 8: Graph of comparison of model accuracy according to experiment. Figure 6: The learning curve for the model using only FER2013 image data. Figure 7: The learning curve for the model using HRV index and FER2013 images. et al. [22] achieved 72.16%; [PERSON] et al. [19] reached 72.3%; and [PERSON] et al. [42] recorded 73.28%. Additionally, by integrating physiological signals with facial expressions, our approach reached an accuracy of 88% on the same dataset, outperforming other models. The comparison is based on previous experiments on the FER-2013 dataset shown in Table 3.  
HRV is traditionally measured in clinical settings, using ECG in which special electrodes are affixed to the patient's body. This method, while accurate, requires access to medical facilities and equipment. However, advancements in technology have introduced more accessible ways to obtain HRV measurements. For example, signals can be received using chest straps. These devices allow for the continuous monitoring of HRV without the need for a hospital visit. Users can simply wear the chest strap, which collects ECG signals through sensors. This method offers convenient monitoring of emotional states or stress levels in real time, outside of a clinical setting. Moreover, the field of remote photoplethysmography (rPPG) is gaining traction among researchers for its non-invasive approach to health monitoring. rPPG technology enables the detection of various physiological parameters--including heart rate, blood pressure, oxygen saturation, and HRV--by observing the color changes in the face that correspond with the blood pulse. rPPG opens up a new path for remote health monitoring, offering a convenient way to assess physical conditions. The combination of HRV data obtained through rPPG with emotions obtained from facial expression methods can enhance the accuracy of emotion detection systems. While facial expressions can provide valuable information about an individual's emotional state, they can sometimes be misleading or difficult to interpret due to various factors such as cultural differences or individual differences in emotional expression. By incorporating physiological signals like HRV, which reflect the autonomic nervous system's response to emotional stimuli, researchers can develop a deeper understanding of human emotions. This approach, combining visual signs with physiological data, holds promise for improving the reliability of emotion detection technologies, potentially benefiting a wide range of applications including mental health monitoring and psychological research. Furthermore, the integration of emotion recognition technology into autonomous vehicles helps to promptly detect driver stress and fatigue, which can prevent accidents on the road. By monitoring the driver's emotional and physiological state, the vehicle can identify early signs of tiredness or stress and suggest appropriate measures, such as activating autonomous driving mode to allow the driver to rest or providing directions to the nearest rest area. However, it is essential to consider the limitations of this approach. Although our current dataset is limited to images from the FER2013 dataset alone, we plan to expand it by acquiring additional data.