Turkish Journal of Computer and Mathematics Education
Journal license

Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 11, Issue 3


Published
on


Pages

2838-2848


DOI

Article

Utilizing Machine Learning and Image Processing to Detect Signs of Stress in Individuals


Authors

Srikanth Reddy Konni Affiliation:
Department of CSE, Sree Chaitanya College of Engineering, Karimnagar
and Khaja Ziauddin Affiliation:
Department of CSE, Sree Chaitanya College of Engineering, Karimnagar


Abstract

This study's main objective is to identify stress in the human body using vivid machine learning and image processing techniques. Our system is an improved version of earlier stress detection systems that lacked personal counseling or live detection. Instead, it detects employees' levels of both physical and mental stress and offers appropriate stress management strategies through a survey form. Additionally, our system includes periodic analysis of employees and live detection. To make the most of employees during working hours, our approach is mainly concerned with stress management and fostering a positive, flexible work environment.


Keywords

K-Nearest Neighbor Classifier, Stress, Stress prediction, Facial Expressions


Citation

Konni, S. R. & Ziauddin, K. (2020). Utilizing machine learning and image processing to detect signs of stress in individuals. Turkish Journal of Computer and Mathematics Education, 11(3), 2838–2848.

Published by: Engineering Journals

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