Smart Stress Detection Using IoT and EEG Data: A Review on Machine Learning Approaches

Stress has become a very common issue in modern lifestyle, especially among students and working professionals. Continuous exposure to academic or work pressure often results in physical as well as mental health problems if it is not managed properly. With the fast development of Internet of Things (IoT) and wearable technologies, it has become possible to monitor human emotions in real-time through different physiological signals. This review paper mainly focuses on the integration of IoT-based sensors and EEG data with machine learning models for accurate stress detection. Various studies have shown that features like brainwave patterns, heart rate variability, and electrodermal activity play an important role in understanding stress levels. Machine learning algorithms such as Random Forest, Support Vector Machine, and Gradient Boosting have been widely used to classify stress states efficiently. The use of EEG sensors like NeuroSky Mindwave, connected via Bluetooth, helps in collecting brain signal data that can be processed and visualized using a Python Flask web application. The review also highlights several challenges that still exist in this field such as noisy data, sensor reliability, and limited dataset size which sometimes affect model performance. Moreover, lack of generalization and personalization remains a concern in building robust stress prediction systems. Future research can focus on hybrid machine learning models and explainable AI approaches to make predictions more transparent and accurate. Overall, this paper gives an overview of the latest developments, methods, and technologies used for real-time stress detection and management using IoT and EEG data