Stress Detection

Overview

The "Stress Detection Using AI, ML, and DL" project aims to develop an intelligent system capable of detecting and analyzing stress levels in individuals through the application of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques. Stress is a prevalent issue in modern society, affecting mental and physical health, productivity, and overall well-being.

Early detection of stress can help individuals take proactive measures to manage it, thereby improving their quality of life. This project leverages advanced computational methods to create a robust and accurate stress detection system that can be applied in various domains, including healthcare, workplace environments, and personal wellness. The project begins with data collection, where physiological and behavioral data are gathered from individuals.

This data may include heart rate variability (HRV), electrodermal activity (EDA), facial expressions, voice patterns, and even social media activity. These datasets are then preprocessed to remove noise and irrelevant information, ensuring that the input data is clean and suitable for analysis. Feature extraction is a critical step, where meaningful attributes are identified and extracted from the raw data. These features serve as the foundation for training the AI models.

Machine Learning algorithms, such as Support Vector Machines (SVM), Random Forests, and Gradient Boosting, are employed to classify stress levels based on the extracted features. These models are trained on labeled datasets, where each data point is associated with a specific stress level (e.g., low, medium, high). The performance of these models is evaluated using metrics like accuracy, precision, recall, and F1-score. To enhance the system's accuracy, Deep Learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are utilized.