Detection methods for a low-cost accelerometer-based approach for driver drowsiness detection

Thousands of accidents and fatalities occur each year due to drowsy and fatigued drivers who choose to operate motor vehicles despite their reduced level of alertness. Actively monitoring Steering Wheel Movements (SWM) has been an important and well documented method for the detection of drowsy driving. Despite the efficacy of the SWM method, it has yet to be widely deployed widely on motor vehicles as a practical means for individual early detection due to the cost prohibitive nature of current methods as well as complexity of installation and implementation. Due to these limitations, potentially lifesaving methods based on SWM monitoring have not been widely implemented. This paper assesses the efficacy of a proposed low-cost accelerometer-based method of SWM monitoring by extracting various SWM parameters and using the extracted data to train machine learning algorithms. Experimental results suggest that the use of adequately trained Support Vector Machines with Accelerometer-based SWM can be a valuable tool in the detection of drowsy driving and the reduction in death and injuries.