R
Rahul Banerjee
Researcher at Birla Institute of Technology and Science
Publications - 23
Citations - 391
Rahul Banerjee is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Wearable computer & Wireless ad hoc network. The author has an hindex of 9, co-authored 20 publications receiving 316 citations. Previous affiliations of Rahul Banerjee include LNM Institute of Information Technology.
Papers
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Journal ArticleDOI
A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals
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.
Proceedings ArticleDOI
Detection of fatigue of vehicular driver using skin conductance and oximetry pulse: a neural network approach
Mahesh Bundele,Rahul Banerjee +1 more
TL;DR: Using Neural Network approach, Multilayer Perceptron Neural Networks (MLP NN) have been designed to classify Pre and Posting driving fatigue levels and it was discovered that the performance of one hidden layer based MLP Nn is comparable to the two hidden layers based MLp NN and there is slight rise in PCLA from One hidden layer to two hidden layer.
Proceedings ArticleDOI
An SVM Classifier for Fatigue-Detection Using Skin Conductance for Use in the BITS-Lifeguard Wearable Computing System
Mahesh Bundele,Rahul Banerjee +1 more
TL;DR: This paper is an attempt towards finding the correlation of skin conductance with the fatigue of a driver.
Proceedings ArticleDOI
An approach for real-time stress-trend detection using physiological signals in wearable computing systems for automotive drivers
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.
Journal ArticleDOI
Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios
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.