Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
From Research to Classroom: A Course in Pervasive Computing
IEEE Pervasive Computing
The ANN-based computing of drowsy level
Expert Systems with Applications: An International Journal
Driver drowsiness detection with eyelid related parameters by Support Vector Machine
Expert Systems with Applications: An International Journal
EEG-based subject- and session-independent drowsiness detection: an unsupervised approach
EURASIP Journal on Advances in Signal Processing
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Optimal time segments for stress detection
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Vehicular accidents are increasingly contributing towards loss of lives across the world. Timely detection of physiological and psychological parameters of the vehicular driver, which could cause various levels of physical and mental fatigue that lead to slower reflexes is therefore extremely important. As part of an ambitious research initiative, India is developing a pervasive computing solution for eliminating / reducing such accidents. As one of the component of such solution, a wearable computing system has been envisioned to be worn by the driver. A complex set of noninvasive and nonintrusive sensor-compute element integrated with appropriate e-textile would form the primary part of this wearable computer. Out of the initial set of physiological parameters such as Skin Conductance, Oximetry Pulse, Respiration, SPO2, the current work focuses on the first two parameters to detect and monitor the mental fatigue / drowsiness of a driver. Using Neural Network approach, Multilayer Perceptron Neural Networks (MLP NN) have been designed to classify Pre and Posting driving fatigue levels. The performance of single hidden layer and two hidden layers based MLP NN have been discussed using the performance measures such as, Percentage Classification Accuracy (PCLA), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Area under Receiver Operating Characteristic Curve (AROC), Area under Convex Hull of ROC (AHROC). 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.