Stress Detection in IT Professional by Image Processing and Machine Learning

K Naresh and Gettam Rishitha

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 3, 238-247

Abstract: The detection of stress levels in an employee of the IT industry can become one great measure to ensure mental wellness and productivity of these employees, while modern technology has evolved rhymingly with another concern. The project under consideration proposes an integrated machine learning image processing method that works for efficient prediction of stress. Initially-a K-Nearest Neighbors algorithm trained with certain physiological parameters such as snoring range, respiration rate, body temperature, number of sleeping hours, and heart rate-for the early recognition of stress symptoms. In addition, there will be an experimental model based on Convolutional Neural Networks for emotion graph classification from facial expressions for sensing these stress indicators. Integration with an online camera module allows further real-time online monitoring of stress percentages based on facial recognition. Hence, by merging physiological signal analysis and image-based emotion recognition, the totality of this framework constitutes a complete non-invasive stress detection scheme. The innovation aims to assist in the proactive handling of stress by IT professionals, thereby ensuring better workplace performance.

Keywords: Mine detection; machine learning approaches; deep learning models; k-nearest neighbors (knn); convolutional neural networks (cnn) in emotion recognition; physiological parameters; face expression analysis about DeepFace; mental health; non-invasive detection; image processing; biometric data (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i3:id:820

DOI: 10.32628/IJSRST2512329

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