Recognising driver anger using multiple physiological signals

Anger is particularly important to recognise accurately and effectively as the most important negative emotion affecting driving safety and the driving experience. In this study, five physiological signals, namely RESP, ECG, EDA, PPG and EMG, were combined to identify driving anger, in order to construct a multi-physiological emotion recognition model and establish the correspondence between emotion and physiological indicators. The data were collected from the simulated driving experimental environment. Firstly, happy emotion, angry emotion and neutral emotion, were induced by driving contextual video stimulus materials and non-driving contextual video stimulus materials, and the effects of the induced anger emotions were compared. Second, physiological sensors were used to collect the physiological data of the subjects under different emotions, the SAM scale was filled in to measure the degree of emotion evoked in the subjects, and then one-to-one correspondence between the subjects' emotions and physiological indexes was carried out, so as to construct the physiological and emotional data sample library. Finally, three algorithms, namely, decision tree, support vector machine and LightGBM, were used to process the collected physiological data to further classify and identify the emotions. The results show that the recognition accuracy of the classification task is improved by 4.43% on comparing with the results before feature selection, and this method verifies that it is feasible to use the LightGBM model as the emotion recognition model, which can provide the technical implementation basis for the emotion prediction and emotion regulation model in the subsequent research.