Python Based Stress Detection System Using Machine Learning & Algorithm’s

In today’s high-pressure world, the need for innovative, non-intrusive stress monitoring solutions is more critical than ever. This paper introduces a sophisticated stress detection system that combines facial expression analysis with machine learning to deliver real-time, continuous stress assessment. Our system employs Haar Cascade classifiers for precise face detection, CNN for feature extraction and utilizes the K-Nearest Neighbors (KNN) algorithm to classify stress-related facial expressions. A significant advantage of our system is its non-intrusive nature, relying solely on visual data, thus eliminating the need for cumbersome wearable sensors. This enhances user comfort and convenience. This paper outlines the design, implementation, and evaluation of our stress detection system, emphasizing its potential applications in workplace wellness, mental health monitoring, and human-computer interaction