A major research focus in automobile development is improvement of safety. The main cause for road accidents is the distractions to the driver. Most distractions are in the form of emotional changes that result in unfitting states of mind. Existing methods of detecting a sleepy driver using image processing are proven to be challenging in practice due to the variations in the lighting condition. Further, it is insufficient to detect sleepiness
and fatigue as there are several other emotional conditions which could cause a driver to be in an unfitting state for driving. Such states of the driver could be identified using basic parameters of an ECG. In this research, different patterns in the ECG of the driver and patterns in the motion of the vehicle were identified for each emotional state to predict the driver’s emotional condition and warn if it tends to unsafe driving. Patterns in the
motion of the vehicle were analyzed in terms of the vehicle speed and the change in the acceleration. A Heart and Brain SpikerShield was used to obtain the ECG of the driver and an MPU-6050 IMU was used to gather the acceleration data of the vehicle. Collected data is sent to an Android device via Bluetooth for further processing. We were able to recognize the changes in the ECG and the driving pattern of a drowsy driver and an
aggressive driver. Accuracy of the emotion detection was verified by comparing the results against known methods.