No abstract

No abstract

There is ample evidence confirming the adverse effects of negative emotions such as anger, fear, and anxiety on drivers’ performance. Also, effectiveness of biological signals in emotion recognition has been confirmed. Therefore, developing advanced driver-assistance systems based on biological signals to detect negative emotions can play a major role in improving driving safety. However, since recording signals, data analysis, as well as design and implementation of a system based on one or more biological signals take time and are costly, it is necessary to conduct appropriate preliminary studies on the efficiency of these signals in identifying negative emotions. The purpose of this study was to explore the efficiency of four biological signals including electrocardiogram (ECG), electromyogram (EMG), electrodermal activity (EDA), and electroencephalogram (EEG) in detecting negative emotions while driving. To this end, a series of scenarios were designed to arouse negative emotions in the driving simulator environment. A total of 43 individuals participated in the experiments, during which the four signals were recorded. Next, we extracted 58 features from the collected data for analysis. Then, multi-layer perceptron and radial basis function neural networks were implemented using the features of each of these signals separately. Afterward, the four evaluation criteria of accuracy, sensitivity, specificity, and precision were calculated for the signals. Finally, TOPSIS was used to rank the signals. ECG and EDA signals, with 88% and 90% accuracy, respectively, were found to be the best signals in detecting negative emotions during driving.

No abstract

No abstract