Open AccessDriver emotion recognition of multiple‐ECG feature fusion based on BP network and D–S evidence
Author(s) -
Wang Xiaoyuan,
Guo Yongqing,
Ban Jeff,
Xu Qing,
Bai Chenglin,
Liu Shanliang
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0499
Subject(s) - anxiety , feature (linguistics) , judgement , cognition , computer science , emotion recognition , artificial intelligence , engineering , psychology , linguistics , philosophy , neuroscience , psychiatry , political science , law
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.