Book Title: Contemporary Research Across Disciplines
Editors: Dr. R. Saravana Selvakumar and Mr. R. Venkatesan
ISBN: 978-81-978738-1-2
Chapter: 24
DOI: https://doi.org/10.59646/crc24/278
Author: Mr. K. Manikandan, Assistant Professor, Department of Information Technology, G. Venkataswamy Naidu College (Autonomous), Kovilpatti, Tamil Nadu, India
Abstract
Our project aims to identify IT professionals’ stress levels using advanced machine learning and image processing methods. Our system enhances previous stress detection models by incorporating live detection and personal counselling, which were previously excluded. It offers continuous employee monitoring and evaluation, identifying physical and mental stress levels. Additionally, it provides regular surveys and appropriate solutions to help manage stress effectively. Our system is designed to create a healthier, more dynamic work environment, ensuring employees perform at their best during working hours.
References
Bakker, J., Holenderski, L., Kocielnik, R., Pechenizkiy, M., Sidorova, N. “Stess@ work: From measuring stress to its understanding, prediction, and handling with personalized coaching.” In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. ACM; 2012, p. 673–678.
Bhattacharyya, R., & Basu, S. (2018). Retrieved from ‘The Economic Times’.
Deng, Y., Wu, Z., Chu, C.H., Zhang, Q., Hsu, D.F. “Sensor feature selection and combination for stress identification using combinatorial fusion.” International Journal of Advanced Robotic Systems 2013;10(8):306.
Ghaderi, A., Franchi, J., Farnam, A. A. “Machine learning-based signal processing using physiological signals for stress detection.” In: 2015 22nd Iranian Conference on Biomedical Engineering (ICBME). 2015, p. 93–98.
Giannakakis, G., Manousos, D., Chiarugi, F. “Stress and anxiety detection using facial cues from videos.” Biomedical Signal Processing and Control, vol. 31, pp. 89-101, January 2017.
Jick, T., and Payne, R. “Stress at work.” Journal of Management Education, vol. 5, no. 3, pp. 50-56, 1980.
Liu, D., Ulrich, M. M. “Listen to your heart: Stress prediction using consumer heart rate sensors.” 2015.
Manikandan, A., & Bindu, M. R. (2023). Fake image verification using machine learning. Journal of Inventive and Scientific Research Studies, Vol. I, Issue 1, 168–174.
Nakashima, Y., Kim, J., Flutura, S., Seidler, A., Andre, E.. “Stress recognition in daily work.” In: International Symposium on Pervasive Computing Paradigms for Mental Health. Springer; 2015, p. 23–33.
Nisha Raichur, Nidhi Lonakadi, Priyanka Mural. “Detection of Stress Using Image Processing and Machine Learning Techniques.” vol. 9, no. 3S, July 2017.
OSMI Mental Health in Tech Survey Dataset, 2017. https://www.kaggle.com/qiriro/stress
Reddy, U. S., Thota, A. V., and Dharun, A. “Machine Learning Techniques for Stress Prediction in Working Employees.” 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, 2018, pp. 1-4.
Subashree, R. R., & Jasmine Goldena, N. (2024). An overview of deep learning in medical image processing using CNN. Journal of Inventive and Scientific Research Studies, Vol. II (Issue 1), 56-71.
Villarejo, M.V., Zapirain, B.G., Zorrilla, A.M. “A stress sensor based on galvanic skin response (GSR) controlled by ZigBee.” Sensors 2012; 12(5):6075–6101.
World Health Organization. “World health report.” Communications, N. 2001. URL: http://www.who.int/whr/2001/media centre/press_release/en/
Xu, Q., Nwe, T.L., Guan, C. “Cluster-based analysis for personalized stress evaluation using physiological signals.” IEEE journal of biomedical and health informatics, 2015;19(1):275–281.