Rajiv Ranjan Singh

TL;DR: This work proposes a neural network driven based solution to learning driving-induced stress patterns and correlating it with statistical, structural and time-frequency changes observed in the recorded biosignals, concluded that Layer Recurrent Neural Networks are most optimal for stress level detection.

TL;DR: A stress-trend detection by the mesh of embedded sensory elements residing in the e-fabric of a wearable computing system will help in reducing chances of fatal driving errors by the way of in-time activation of alerts and actuation of corresponding safety / recovery procedures.

TL;DR: The proposed framework will enable proactive initiation of rescue and relaxation procedures during accidents and emergencies by identifying four stress-classes using cascade forward neural network (CASFNN) which performed consistently with minimal intra- and inter-subject variability.

TL;DR: A cumulative sum-based stress metric, capable of detecting over-stress conditions, was developed using Page?s Technique and will help in identification of stressful situations where the driver is susceptible to temporal loss of concentration and vehicle control.

TL;DR: In order to estimate the mental and physical fatigue to which a driver may be subjected to, the authors collected GSR, SpO2, Respiration, and ECG signals during relaxed and stressful driving scenarios.