ML-Driven Real-Time Emotion and Stress Level Monitoring

The growing global prevalence of stress-related disorders has necessitated the development of advanced, noninvasive technologies for real-time stress detection. Traditional stress monitoring methods often require physiological sensors, which, while accurate, can be intrusive and impractical for everyday use. In this paper, we present an innovative approach that leverages deep learning and facial emotion recognition to detect stress levels dynamically and non-invasively. Our system is based on a Convolutional Neural Network (CNN) trained using the FER-2013 dataset, which contains a wide variety of facial expressions. The model is capable of classifying seven core emotions such as anger, disgust, fear, happiness, sadness, surprise, and neutral with high accuracy. Each of these emotions is then mapped to corresponding stress levels (low, moderate, or high), allowing for real-time stress evaluation through facial expressions alone. A key feature of our proposed system is its user-centric design, which includes a real-time visual feedback mechanism. This interface displays stress levels continuously as they are detected, making the system suitable for various practical applications such as mental health monitoring, stress management in workplace environments, and personal well-being tracking. The non-invasive nature of this approach eliminates the need for cumbersome sensors and facilitates its use in dynamic, everyday environments. To validate the effectiveness of the system, extensive experiments were conducted to assess both emotion recognition accuracy and the reliability of stress-level detection. The model achieved a significant l e vel o f p e rformance, d e monstrating t h e v i ability of using facial expressions as a surrogate for physiological stress markers. Additionally, we discuss potential real-world applications, where this system could be integrated into wearable devices or smartphone applications, providing continuous, unobtrusive stress monitoring. The use of deep learning for emotion-based stress detection represents a significant a d vancement i n the field, o f fering a n a c cessible, s c alable s o lution f o r a d dressing the growing public health challenge of stress.