Stress Detection through Heart Rate Variability and Live Facial Expression Analysis

DOI

https://www.doi.org/10.59256/ijrtmr.20250504002

Published In

Volume 5, Issue 4 (2025)

Published Online

July-August 2025

Pages

14-20

Article Type

Original Article

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The main motive of our project is to detect stress in the IT professionals using vivid Machine learning and Image processing techniques. Our system is an upgraded version of the old stress detection systems which excluded the live detection and the personal counseling but this system comprises of live detection and periodic analysis of employees and detecting physical as well as mental stress levels in his/her by providing them with proper remedies for managing stress by providing survey form periodically. Our system mainly focuses on managing stress and making the working environment healthy and spontaneous for the employees and to get the best out of them during working hours.Stress is a pervasive factor influencing human health, cognitive performance, and emotional well-being. With the increasing demand for non-invasive and real-time mental health monitoring systems, this study presents a hybrid approach for stress detection through the integration of Heart Rate Variability (HRV) analysis and live facial expression recognition. HRV is a widely accepted physiological indicator of autonomic nervous system activity, and its metrics—such as RMSSD, SDNN, and LF/HF ratio—offer reliable insights into stress levels. Complementing this, facial expressions provide observable and immediate emotional cues that can reflect psychological states. In this system, HRV data is collected via wearable sensors, while facial expressions are captured through live video input. Deep learning models, particularly convolutional neural networks (CNNs), are employed to classify emotions such as anger, sadness, and fear, which are often correlated with stress. A multimodal fusion algorithm combines HRV features and facial emotion data to compute a real-time stress score, enhancing overall accuracy and robustness compared to single-modality systems.The proposed system is designed for real-time operation, offering continuous monitoring and early detection of stress. It is suitable for applications in healthcare, workplace wellness, education, and personal mental health management. Future enhancements could include the incorporation of additional biosignals, improved emotion recognition across diverse populations, and context-aware analysis. By integrating physiological and behavioral data, this approach aims to provide a more holistic, accurate, and accessible solution for stress detection and management in everyday environments