This project develops a stress detection pipeline from wearable sensor data using physiological signals such as blood volume pulse and electrodermal activity. It expands feature extraction, applies subject-specific normalization, and uses balanced learning with leave-one-subject-out validation to avoid data leakage. The system evaluates multiple classifiers and window sizes, achieving robust and reliable performance, while analysis of features and errors improves interpretability and real-world applicability.