An SVM Classifier for Fatigue-Detection Using Skin Conductance for Use in the BITS-Lifeguard Wearable Computing System

Monitoring driver fatigue, inattention, drowsiness and alertness is very important in order to prevent vehicular accidents. The system detecting and monitoring should be noninvasive type and non-distracting to the driver. The physiological parameters such as skin conductance, oximetry pulse, respiration, SPO2 and BVP can lead to the acceptable solution to the problem. The author is working on the subset of the project 'BITS Life Guard system' and trying to correlate the fatigue of a driver with the set of physiological parameters so as to fulfill the requirements. This paper is an attempt towards finding the correlation of skin conductance with the fatigue of a driver. Artificial Neural Network approach is used to design the system by taking actual body parameters of the drivers under different state of work & environment. Multilayer Perceptron (MLP) Neural Network (NN) and the Support Vector Machine (SVM) are used to correlate the driver's fatigue level with skin conductance. Two state classifiers were designed and tested with 18 input features for 2392 total data rows and found that SVM gives a better Classification Accuracy. The performance measures used for designing are Percentage Classification Accuracy (PCLA), Mean Square Error (MSE) and Receiver Operating Characteristics (ROC).

[1]  Pasquale Daponte,et al.  ANN-based error reduction for experimentally modeled sensors , 2000, Proceedings of the 17th IEEE Instrumentation and Measurement Technology Conference [Cat. No. 00CH37066].

[2]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[3]  Rahul Banerjee.  From research to classroom a course in pervasive computing , 2005, IEEE Pervasive Comput..

[4]  Petro M. Gopych.  ROC Curves Within the Framework of Neural Network Assembly Memory Model: Some Analytic Results , 2003, ArXiv.

[5]  Iryna V. Turchenko.  Simulation Modeling of Mullti-Parameter Sensor Signall Identification Using Neural. Networks , 2004 .

[6]  Necmettin Sezgin,et al.  The ANN-based computing of drowsy level , 2009, Expert Syst. Appl..

[7]  Shuyan Hu,et al.  Driver drowsiness detection with eyelid related parameters by Support Vector Machine , 2009, Expert Syst. Appl..

[8]  Abdulhamit Subasi,et al.  Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients , 2005, Expert Syst. Appl..

[9]  Kevin W. Bowyer,et al.  Generating ROC curves for artificial neural networks , 1994, IEEE Transactions on Medical Imaging.

[10]  J. M. DeLeo,et al.  The receiver operating characteristic function as a tool for uncertainty management in artificial neural network decision-making , 1993, 1993 (2nd) International Symposium on Uncertainty Modeling and Analysis.

[11]  V. Kvasnicka,et al.  Neural and Adaptive Systems: Fundamentals Through Simulations , 2001, IEEE Trans. Neural Networks.

[12]  Feng Jiao,et al.  Locating Eye and Nose Features Precisely in IR Video Sequences for Predicting Driver Fatigue , 2007 .

[13]  Sanjay V. Dudul,et al.  Neural Network based Sensor for Classification of Material Type and its Surface Properties , 2007, 2007 International Joint Conference on Neural Networks.

[14]  Suresh S. Salankar,et al.  SVM Based Model as an Optimal Classifier for the Classification of Sonar Signals , 2007 .

[15]  Eric B. Baum,et al.  On the capabilities of multilayer perceptrons , 1988, J. Complex..

[16]  Mervyn V. M. Yeo,et al.  Can SVM be used for automatic EEG detection of drowsiness during car driving , 2009 .

[17]  Andry Rakotonirainy,et al.  Affordable visual driver monitoring system for fatigue and monotony , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).