Monitoring the Fatigue Conditions and Controlling the Speed of the Vehicle

A monitoring system is designed in where it receives sensory data via wireless sensor network and further processes the data to indicate the current driving aptitude of the driver. Analyzing information related to fatigue using two distinct methods eye movement monitoring and bio signal processing. It is critical that several sensors are integrated and synchronized for a more realistic evaluation of the driver behaviour. The sensors applied include a video sensor to capture the driver image and a bio signal sensor to gather the driver photoplethysmograph signal. A warning alarm is sounded if driver fatigue is believed to reach a defined threshold. The manifold testing of the system demonstrates the practical use of multiple features, particularly with discrete methods, and their fusion enables a more authentic and ample fatigue detection. Introduction The growth of sensor technology and network based information technology has expanded the reach of wire-less sensor networks into numerous areas such as health care, remote control, wildlife habitat monitoring, military explosive detection, intelligent home monitoring and environment observation and forecasting system. On the other hand, the recent increase in traffic accidents is possibly four distinctive driving patterns through analysis by a hidden Markov model (HMM) studied the reliability of steering behaviour to detect caused by driver distraction and low attention during driving. Intelligent transport systems are promoted by integrating the sensor technology in to the transport to measure the driver alertness level. A. Nivetha et. al. 2 A nonintrusive prototype computer vision system has been proposed for monitoring driver’s attentiveness in real-time. Kasukabe‘s manuscript was supported in part by the Ministry of Education, Science and Technology, and by the National Research Foundation of Korea through the Human Resource Training Project for Regional Innovation. Pauwelussen [7] developed a traffic simulation model in which a vehicle is equipped with an adaptive cruise-control (ACC) and lane-departure warning (LDW) system to monitor driver behaviour in a real traffic environment. Moreover, Lee [8] proposed a system with two fixed cameras to capture images of the driver and the road respectively, and then the images are mapped to global coordinates to monitor the driver sight line. The authors found driver fatigue by multi wavelet packet energy spectrum using a support vector machine (SVM). Lee [10] developed a video sensor based eye-tracking and blink-detection system with Haar like features and template matching for an automated drowsiness warning system. In addition, Yang demonstrated that drowsiness has a greater effect on rulebased driving tasks than on skill-based tasks using a Bayesian network (BN) paradigm through simulator based human in the loop experiments. A webcam is placed on the dashboard in front of the driver to capture the driver image whereas a PPG sensor is installed at the steering wheel to collect the driver PPG signals through a finger underneath skin. The smart phone received the signals via connected wireless sensor. Figure 1: Flow Chart of System Flow Design. In figure 1, the flowchart of the entire system is given. Here the driver’s image and bio signals were taken from the webcam and from the bio sensor respectively. Then from the input image, the features such as eye blink rate detection, percentage of eye closure are extracted and from the bio sensor the bio signal like photoplethymography signal will be extracted. Once if the fatigue is believed a warning will be given. Monitoring the Fatigue Conditions and Controlling the Speed of the Vehicle 3