Machine Learning-Based Stress Assessment Using Wearable Sensors

In the realm of mental health management, early detection and monitoring of stress levels can significantly impact overall well-being. This project presents a novel approach to assessing mental stress using a combination of wearable sensors and machine learning techniques. We utilized a suite of sensors including a Galvanic Skin Response (GSR) sensor, a pulse sensor, and a Dallas BMT180 temperature sensor to gather physiological data indicative of stress. The data collected from these sensors are processed and analyzed using machine learning algorithms to predict stress levels accurately. To facilitate real-time monitoring, an LCD display is employed to present the sensor data, while a GSM module is integrated to send alert messages in the event of detected stress. This system aims to provide an effective and responsive solution for stress management by combining advanced sensor technology with intelligent data analysis and real-time communication. The integration of these components not only enhances the accuracy of stress detection but also ensures timely intervention, promoting better mental health outcomes.