Edge Computing-Enabled Stress Detection through Emotion-Classified CNN


  • K.N Apinaya Prethi, S.M Nithya, T. Jayanthi, S. Hariharan


Stress detection, Cloud-Edge computing, Facial Expressions, Deep learning, Emotion classification, Personalized treatment


Modern society is extremely stressful. As well, the environment in which we live does nothing to aid individuals; rather, it pushes us over the brink and adds to our stress levels. Increased stress can lead to mortality in certain extreme cases; therefore an image-based stress detection system was created for Cloud-Edge computing that is non-invasive. A person's stress is expressed through facial expressions. Hence, in this paper deep learning algorithm is employed on facial photos to classify emotions for stress detection. The proposed neural network for emotion classification achieved an accuracy of 88%. The classified emotions were then fed into a stress detection module which detects the subjected individual as stressed if more than 75% of the classified emotions fall under the stressful emotions such as anger, sadness, disgust, and fear. These emotions are identified as high priority tasks which will help to provide personalized treatment by using edge devices.


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How to Cite

K.N Apinaya Prethi*1,. (2024). Edge Computing-Enabled Stress Detection through Emotion-Classified CNN. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2151–2158. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5808



Research Article