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Designing a wearable driver assist system requires extraction of relevant features from physiological signals like galvanic skin response and photoplethysmogram collected from automotive drivers during real-time driving. In the discussed case, four stress-classes were identified using cascade forward neural network (CASFNN) which performed consistently with minimal intra- and inter-subject variability. Task-induced stress-trends were tracked using Triggs’ Tracking Variable-based regression model with CASFNN configuration. The proposed framework will enable proactive initiation of rescue and relaxation procedures during accidents and emergencies.