Happy-anger emotions classifications from electrocardiogram signal for automobile driving safety and awareness

Developing a system to monitor the physical and psychological states of a driver and alert the driver is essential for accident prevention. Inspired by the advances in wireless communication systems and automatic emotional expression analysis using biological signals, an experimental protocol and computational model have been developed to study the patterns of emotions. The goal is to determine the most efficient display stimuli to evoke emotions and classify emotions of individuals using electrocardiogram (ECG) signals. A total of 69 subjects (36 males, 33 females) participated in the experiment and completed the survey. Physiological changes in ECG during the stimulus process were recorded using a wireless device. Recorded signals underwent a filtering process and feature extraction to determine meaningful features, define the model based on data assumption, and finally select algorithms used in the classification stage. Two extracted ECG features, namely root mean square successive difference and heart rate variability, were found to be significant for emotions evoked using the display stimuli. Support vector machine classification results successfully classify the happy-anger emotions with 83.33% accuracy using an audio-visual stimulus. The accuracy for happy recovery is 90.91%, and an excellent accuracy was also acquired for anger recovery. Findings of this work show that ECG can be used as an alternative to automatic self-reflective test procedures or additional source with which to validate the emotional state of a driver while in an automobile.