This thesis investigates automatic stress recognition from non-invasive, wearable physiological signals using two complementary paradigms: a feature–driven (FD) pipeline based on handcrafted descriptors and classical machine learning, and a data–driven (DD) one-dimensional convolutional neural network (1D-CNN) trained end-to-end. Multi-modal data include electrodermal activity, photoplethysmography, accelerometry, and skin temperature. A unified pre-processing and windowing scheme is adopted; the FD branch extracts time-, frequency-, and non-linear features, followed by model selection among tree ensembles, linear models, and kernels. The DD branch employs a compact CNN architecture with modality-specific streams and late fusion. Robustness is assessed through an in-domain evaluation and a comprehensive cross-dataset protocol spanning four corpora with heterogeneous elicitation protocols and sensors (WESAD, CAMPANELLA, VerBIO, AffectiveROAD).