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).