Real-Time Stress Detection Using Webcam-Based Physiological Signals and Interpretable ML

Webcam-based physiological Signals Screen in Real-Time for Stress and Interpretable Machine Learning began a non-invasive solution to strain checking owing to a webcam. With the increasing number of stress-related diseases, the method proposed here is a worthwhile one for early intervention and mental health management. The real-time stress level can be successfully measured with the highest degree of accuracy by getting data, including facial expressions, heart rate, and respiration rates, and analyzing it through complicated machine learning models. Research has presented that the classification rate can reach hight rate, making it an ideal technique for discovering stress through deep learning frameworks. These tests involve actions that can be planned ahead of time and become adaptable to the environments, such as virtual consultations through the Internet. Thus, this will boost the mental health in the following areas. However, problems involving privacy violations, ethical issues, and technical hitches are testing grounds that must be solved to ensure responsible installation. Future studies should focus on forming new procedures, raising people’s interest, and finding a way to link technological improvements with moral principles. The rapid and timely identification of stress in different work and healthcare settings and state-of-the-art telemedicine systems guarantee enormous advantages in this area. This paper aims to introduce an interpretable machine learning algorithm using webcam-based physiological signals, which can recognize the existence of stress in real time and be a non-invasive and immediate system to address mental illnesses.