CV-SAS: Computer Vision Modeled Seamless Stress Analysis System for Workers

B. Muthu Senthil *, Dinesh E. **, Dinesh T. ***, Goutham Raj S. ****
*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, India.
Periodicity:July - December'2020
DOI : https://doi.org/10.26634/jpr.7.2.18127

Abstract

People spend most of their time in the workplace, often with high workloads and time pressure, a practice that contributes to increased stress levels. An accurate stress assessment method may thus be of importance to clinical intervention and diseases prevention. While different neuro imaging modalities have been proposed to detect mental stress, each modality experiences certain limitations. This paper proposes the ability of a computer to detect and analyze the stress in human beings with greater accuracy. The system uses the webcam to detect the facial features and perform a mood analysis, which is eventually used for detecting stress. Then, the features are fed into the SVM model as an input and using the machine learning approach the stress is classified.

Keywords

Computer Vision, Mental Stress, Real Time Stress Analysis, Machine Learning, Sentiment Analysis, SVM Model.

How to Cite this Article?

Muthusenthil, B., Dinesh, E., Dinesh, T., and Raj, S. G. (2020). CV-SAS: Computer Vision Modeled Seamless Stress Analysis System for Workers. i-manager's Journal on Pattern Recognition, 7(2), 29-36. https://doi.org/10.26634/jpr.7.2.18127

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