Human Stress Detection

Stress has become an increasingly prevalent concern in modern society, impacting mental well-being and overall health. To address this issue, this paper focuses on developing a comprehensive stress detection system. This system leverages machine learning techniques and integrates seamlessly into a user-friendly web application with a secure and efficient backend infrastructure. The proposed system begins with the careful preprocessing of a curated dataset, ensuring the quality of input for machine learning model training. State-of-the-art algorithms are employed to create an accurate stress prediction model. The backend infrastructure is designed to handle user authentication, session management, and communication with the machine learning model, prioritizing data privacy and confidentiality. The proposed work develops a stress detection system that provides essential insights into personal stress levels, facilitating prompt interventions and supporting improved mental well-being. The dataset used for this study is meticulously curated and includes various key factors associated with sleep patterns and stress levels.