Identification of Emotional Stress Detection Using Convolutional Neural Network

Abstract:-The ubiquity of emotional stress has become a major worry affecting people's general well-being and quality of life in today's fast-paced environment. Managing health disorders due to stress requires early detection and management. In this paper, we employ Convolutional Neural Network (CNN) technology to suggest a unique method for identifying emotional stress. The goal of the proposed solution is to create a reliable and accurate system that can recognize emotional stress patterns facial expressions into seven emotion categories. A sizable dataset of labeled stress and nonstress samples taken from people in a variety of real-world situations is used to train the suggested CNN model. The model is intended to separate the input signals into discrete stress categories by extracting pertinent features from the signals. The effectiveness of the CNN-based stress detection system is evaluated by a comprehensive set of tests and evaluations, which include receiver operating characteristic (ROC) curve analysis, sensitivity, specificity, and accuracy. The outcomes show how well and consistently the suggested method works to identify emotional stress levels. The system's usefulness in encouraging proactive stress management techniques and a healthy lifestyle is highlighted by discussing its possible applications in wearable technology and mobile health (mHealth) platforms. All things considered, this study advances the development of technologically mediated approaches to emotional distress and improves people's quality of life in the digital age

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