AI-Enhanced Emotion Recognition through Galvanic Skin Response and ECG Analysis for Improved Accuracy and Reliability

Traditional methods of emotion detection, including the problem of recognizing seven basic emotions, have issues such as occlusion, noise, and self-reporting. Besides text mining there are voices, facial and gesture recognition algorithms as well. The indicated acoustics-based difficulties are resolved in the proposed system by employing the previously mentioned signals - less forgery and interferences for ECG and GSR. When noise from signals is removed, characteristic properties such as pace fluctuation and skin conductance are obtained. All of these features are organized using modern machine learning models such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). Measurements of labelled datasets are utilized to verify the system's accuracy levels, and better performance is reported in the dimension of emotion identification resilience. The proposed methodology in this research is more accurate than other existing systems, with an accuracy rate of 90%, recall rate of 88 percent, and F1-Score of 87.5%. Similar to feature metrics, the level of accuracy for particular parameters has improved. HCl technology will improve learning and mental health features such as recognition, emotion, and social interaction. The established goal of physiological ones is to serve as a solid and steady foundation for identifying emotions.