Driver emotion recognition of multiple-ECG feature fusion based on BP network and D–S evidence

• Published in: IET intelligent transport systems 2020-08, Vol.14 (8), p.815-824
• Main Authors: Wang, Xiaoyuan, Guo, Yongqing, Ban, Jeff, Xu, Qing, Bai, Chenglin, Liu, Shanliang
• Format: Article
• Language: English
• Subjects: anxiety; backpropagation; back‐propagation network; BP network; cognition; Dempster–Shafer evidence method; distracted driving; driver emotion recognition; driver information systems; driver psychological reaction; driving emotion; driving safety; D–S evidence; ECG evidence fusion; ECG signals; electrocardiography; emotion recognition; emotion recognition model; feature extraction; inference mechanisms; medical signal processing; multiple‐ECG feature fusion; multiple‐electrocardiogram feature fusion; nonlinear characteristics; personalised driving warning system; psychology; road accidents; road safety; road traffic; road traffic safety; sensor fusion; Special Section: Sustainably Intelligent Mobility (SIM); time–frequency domain; traffic environment; uncertainty handling; vehicle crash
• ISSN: 1751-956X; 1751-9578; 1751-9578
• DOI: 10.1049/iet-its.2019.0499

Driving emotion is considered as driver's psychological reaction to a change in traffic environment, which affects driver's cognitive, judgement and behaviour. In anxiety, drivers are more likely to get engaged in distracted driving, increasing the likelihood of vehicle crash. Therefore, it is essential to identify driver's anxiety during driving, to provide a basis for driving safety. This study used multiple-electrocardiogram (ECG) feature fusion to recognise driver's emotion, based on back-propagation network and Dempster–Shafer evidence method. The three features of ECG signals, the time–frequency domain, waveform and non-linear characteristics were selected as the parameters for emotion recognition. An emotion recognition model was proposed to identify drivers’ calm and anxiety during driving. The results show after ECG evidence fusion, the proposed model can recognise drivers’ emotion, with an accuracy rate of 91.34% for calm and 92.89% for anxiety. The authors’ findings of this study can be used to develop the personalised driving warning system and intelligent human–machine interaction in vehicles. This study would be of great theoretical significance and application value for improving road traffic safety.