Using Non-deep Learning to Recognize High and Low Valence Emotions on Young Adults by HRV

Emotion recognition plays an important role in understanding human behavior and psychological well-being. In this research, we propose a method to recognize high-valence and low-valence emotions in young adults through the analysis of Heart rate variability, utilizing non-deep learning techniques. The study explores the Young Adult’s Affective Data dataset, comprising physiological information from 25 volunteer participants aged between 8 and 25 years. We employ three non-deep learning classifiers: Support Vector Machine, Logistic Regression, and K-Nearest Neighbors for binary emotion classification. Our method achieved 83% accuracy in recognizing high-valence and low-valence emotions. Overall, our findings highlight the efficacy of HRV-based emotion recognition using non-deep learning techniques, offering promising potential for practical applications in mental health monitoring, affective computing, and human-computer interaction. This study contributes to advancing emotion recognition methods and understanding emotional well-being among young adults.