i-manager Publications

Stress Level Prediction and Monitoring using CNN Model

M. Kishore Babu*, Pranathi Gunnam**, Karavalla Munni***, Tulasi Gurram****, Yaswanth Sai Madala*****, Saikrishna Kancharlapalli******

*-****** Department of Computer Science, Vasireddy Venkatadri Institute of Technology, Andhra Pradesh, India.

Periodicity:April - June'2025
DOI : https://doi.org/10.26634/jit.14.2.21941

Abstract

Stress at work has become a serious problem that affects worker health and business success. Traditional methods of measuring stress, such as self-reports and surveys, are unreliable and may not provide immediate feedback. To overcome these problems, this paper proposes a real-time stress monitoring system that analyzes facial expressions and detects stress using CNNs. The system is suitable for modern workplaces because it uses MobileNetV2 for fast and scalable processing. It also features a chatbot powered by an artificial neural network (ANN) that provides customized stress reduction recommendations, including relaxation techniques and counseling materials. Based on the pilot test results, the system is accurate and efficient, making it a useful tool for managing stress in different work settings.

Keywords

Stress Detection, Convolutional Neural Networks (CNN), MobileNetV2, Artificial Neural Networks (ANN), Flask Framework, Real Time Monitoring.

How to Cite this Article?

Babu, M. K., Gunnam, P., Munni, K., Gurram, T., Madala, Y. S., and Kancharlapalli, S. (2025). Stress Level Prediction and Monitoring using CNN Model. i-manager’s Journal on Information Technology, 14(2), 41-50. https://doi.org/10.26634/jit.14.2.21941

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Stress Level Prediction and Monitoring using CNN Model

Abstract

Keywords

How to Cite this Article?

References

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