Emotion recognition for automotive drivers using simulated driving approach - Ooi, Jonathan Shi Khai (2017)
In last decade, wide range of active safety system had been installed in modern vehicles. Traction control system, auto-braking system, auto wipers and auto lighting are great inventions designed to reduce road accidents. Still, statistics indicates that accident rate in Malaysia had not been compromise despite inclusion of these features. In year 2013, approximately 777,000 registered vehicles were involved in road traffic crashes, with damage cost of more than 9.3 billion Ringgit Malaysia. Automobile network encompasses network between road, vehicles and drivers. Road and vehicles had made great progress, whereas part concerning drivers had left to be the most delicate of this network. This study encapsulates stress and anger as prime emotion encouraging road accident. Electrodermal Activity (EDA) and Electromyography (EMG) of corrugator supercilli had been contemplated for neutral, stress and anger emotion recognition. Simulated driving task with preset scenario had been developed for emotion stimulation. Experimental data were recorded from 20 healthy subjects. Acquired EDA signals were filtered, Short-Time-Fourier- Transformed and had mean, median and variance features extracted, on the contrary, EMG signals were rectified, filtered and had mean, standard deviation and root mean square computed. Recorded EDA and EMG data manifested significant difference (p < 0.05) only between neutral-stress and neutral-anger emotion groups. Regardless, no significant difference (p > 0.05) was perceived between stress-anger groups. Additionally, two-class and multi-class Support Vector Machine (SVM) classification accompanied by cross-validation method had been dispatched to differentiate subjects’ emotion when performing simulated driving task. Dataset from 10 subjects were used for training and another 10 were for testing purpose only. Classification accuracy exceeding 80% had been achieved between neutral-stress and neutral-anger groups when incorporating EDA, less than 70% accuracy was achieved for separation between stress-anger groups. EMG features failed to perform in view of corrugator supercilli may not be compelling measure. This study had incorporated new techniques (Short-Time-Fourier-Transform) for EDA analysis, apart, it is the one of the pioneer study that utilizes EDA for anger emotion recognition, still, classification result acquired is more preferred than past literatures. The research can still be extended by refining signal processing techniques for better classification accuracy and conducting real-world driving experiment for more persuasive result.