A wearable driver assist system using GSR and PPG signals processed by a cascade forward neural network can effectively classify and track driver stress levels in real-time semi-urban driving scenarios.
May support real-time driver stress monitoring; leaves open prospective validation before cardiovascular research or clinical adoption.
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 -based regression model with CASFNN configuration. The proposed framework will enable proactive initiation of rescue and relaxation procedures during accidents and emergencies.
No takes yet. Share an insight, caveat, or question.
Singh et al. (2013) studied this question.
Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context: