Identification of Emotional Stress Detection Using Convolutional Neural Network
Volume: 13 - Issue: 07 - Date: 07-07-2024
Approved ISSN: 2278-1412
Published Id: IJAECESTU411 | Page No.: 106-111
Author: Priya Tiwari
Co- Author: Dr. Indu Shrivastava, Swati Khanve, Dr. Sneha Soni
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
Key Words:-Emotional stress, Convolutional Neural Network (CNN), Stress detection, Physiological signals, Machine learning, , Mobile health (mHealth)
Area:-Engineering
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