Abstract:
This report develops a system to detect stress using wearable devices that monitor Electrodermal Activity (EDA). Our review of related research covers topics like using AI in healthcare, clinical predictions, and emotion detection with wearables. We found that existing methods often lack precise data and struggle with real-world application. Our framework improves on these issues by combining real-time EDA monitoring with a machine learning system that processes the data and identifies stress signals. A key feature of our approach is that it prompts users to provide immediate feedback through a mobile app when stress is detected. This feedback helps refine our system, making it more accurate. By integrating direct user interactions and advanced AI, our project enhances stress management techniques and contributes to better mental health monitoring. This method is straightforward and aims to be easily applicable in daily life, paving the way for future advancements in using wearables to monitor health.
