Abstract:
Stress detection is a critical aspect of mental health monitoring and intervention. This project
introduces a novel approach to stress detection by integrating facial expression analysis with
real-time physiological signal processing. Titled "Stress Monitoring using Facial Expressions,"
the project aims to provide a comprehensive understanding of an individual's stress level by
harnessing multiple data streams.
Traditional stress detection systems often rely solely on facial expressions, which may not
always provide accurate results. To enhance the reliability of stress detection, this project
incorporates real-time monitoring of heart rate (HR) and blood pressure (BP) without the need
for physical sensors. By leveraging advanced computer vision techniques and machine learning
algorithms, facial expressions are analyzed to gauge emotional states, while concurrently, HR
and BP variations are extracted from real time video data.
The integration of facial expression analysis with HR and BP monitoring offers a holistic
approach to stress detection. An increase in HR and BP correlates with heightened stress levels,
providing additional validation to the facial expression analysis. By combining these multimodal
inputs, the system generates a more robust assessment of an individual's stress state.
The proposed system has significant implications in various domains, including healthcare,
workplace wellness, and personal well-being. It offers a non-intrusive and accessible method for
stress monitoring, potentially enabling early intervention and tailored support. The project's
methodology, experimental setup, results, and future prospects are elaborated upon in this report,
highlighting its potential contributions to the field of affective computing and mental health
management
