Emotions Recognition Using Machine Learning For Stress and Anxiety Detection

Abstract

Emotions play a critical role in human well-being, influencing mental and physical health. Stress and anxiety, prevalent emotional states, significantly impact productivity and quality of life, making their early detection vital. This research paper explores the application of machine learning (ML) techniques for recognizing emotions to detect stress and anxiety effectively. By leveraging multimodal data such as physiological signals (e.g., heart rate variability, galvanic skin response), facial expressions, and voice patterns, this study employs advanced ML algorithms to classify emotional states accurately. A comprehensive review of existing literature highlights the limitations of traditional methods and the potential of ML-driven approaches. The proposed methodology includes data preprocessing, feature extraction, and the application of supervised learning models, including Support Vector Machines (SVM), Random Forest, and Convolutional Neural Networks (CNN). Performance evaluation metrics such as accuracy, precision, recall, and F1-score demonstrate the efficacy of the proposed approach. The findings underscore the potential of ML for real-time and non-invasive stress and anxiety detection, with significant implications for mental health monitoring and intervention. This study contributes to bridging the gap between emotion recognition and mental health applications, offering a robust framework for future developments in personalized health care solutions.