Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/166115
Title: On-road experimental study on driving anger identification model based on physiological features by ROC curve analysis
Authors: Ping Wan;Chaozhong Wu;Yingzi Lin;Xiaofeng Ma
Year: 2017
Publisher: IET
Abstract: Road rage is a serious psychological issue affecting traffic safety, which has attracted increasing concern regarding driving anger intervention. This study proposed a method for driving anger identification based on physiological features. First, 30 drivers were recruited to perform on-road experiments on a busy route in Wuhan, China. The drivers&x2019; anger could be inducted on the study route by elicitation events, e.g. vehicles weaving/cutting in line, jaywalking, traffic congestion and waiting at red light if they want to finish the experiments ahead of basic time for extra pay. Subsequently, significance analysis was used to determine that five physiological features including heart rate, skin conductance, respiration rate, the relative energy spectrum of <italic>&x3b8;</italic> and <italic>&x3b2;</italic> bands of electroencephalogram were effective for driving anger identification. Finally, a linear discriminant model was proposed to identify driving anger based on the optimal thresholds of the five features which were determined by receiver operating characteristic (ROC) curve analysis. The results show that the proposed model achieves an accuracy of 85.84% which is 7.95 and 5.71% higher than the models using back propagation neural network and support vector machine, respectively. The results can provide theoretical foundation for developing driving anger detection devices based on physiological features.
URI: http://localhost/handle/Hannan/166115
volume: 11
issue: 5
More Information: 290,
298
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7927519.pdf3.44 MBAdobe PDF
Title: On-road experimental study on driving anger identification model based on physiological features by ROC curve analysis
Authors: Ping Wan;Chaozhong Wu;Yingzi Lin;Xiaofeng Ma
Year: 2017
Publisher: IET
Abstract: Road rage is a serious psychological issue affecting traffic safety, which has attracted increasing concern regarding driving anger intervention. This study proposed a method for driving anger identification based on physiological features. First, 30 drivers were recruited to perform on-road experiments on a busy route in Wuhan, China. The drivers&x2019; anger could be inducted on the study route by elicitation events, e.g. vehicles weaving/cutting in line, jaywalking, traffic congestion and waiting at red light if they want to finish the experiments ahead of basic time for extra pay. Subsequently, significance analysis was used to determine that five physiological features including heart rate, skin conductance, respiration rate, the relative energy spectrum of <italic>&x3b8;</italic> and <italic>&x3b2;</italic> bands of electroencephalogram were effective for driving anger identification. Finally, a linear discriminant model was proposed to identify driving anger based on the optimal thresholds of the five features which were determined by receiver operating characteristic (ROC) curve analysis. The results show that the proposed model achieves an accuracy of 85.84% which is 7.95 and 5.71% higher than the models using back propagation neural network and support vector machine, respectively. The results can provide theoretical foundation for developing driving anger detection devices based on physiological features.
URI: http://localhost/handle/Hannan/166115
volume: 11
issue: 5
More Information: 290,
298
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7927519.pdf3.44 MBAdobe PDF
Title: On-road experimental study on driving anger identification model based on physiological features by ROC curve analysis
Authors: Ping Wan;Chaozhong Wu;Yingzi Lin;Xiaofeng Ma
Year: 2017
Publisher: IET
Abstract: Road rage is a serious psychological issue affecting traffic safety, which has attracted increasing concern regarding driving anger intervention. This study proposed a method for driving anger identification based on physiological features. First, 30 drivers were recruited to perform on-road experiments on a busy route in Wuhan, China. The drivers&x2019; anger could be inducted on the study route by elicitation events, e.g. vehicles weaving/cutting in line, jaywalking, traffic congestion and waiting at red light if they want to finish the experiments ahead of basic time for extra pay. Subsequently, significance analysis was used to determine that five physiological features including heart rate, skin conductance, respiration rate, the relative energy spectrum of <italic>&x3b8;</italic> and <italic>&x3b2;</italic> bands of electroencephalogram were effective for driving anger identification. Finally, a linear discriminant model was proposed to identify driving anger based on the optimal thresholds of the five features which were determined by receiver operating characteristic (ROC) curve analysis. The results show that the proposed model achieves an accuracy of 85.84% which is 7.95 and 5.71% higher than the models using back propagation neural network and support vector machine, respectively. The results can provide theoretical foundation for developing driving anger detection devices based on physiological features.
URI: http://localhost/handle/Hannan/166115
volume: 11
issue: 5
More Information: 290,
298
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7927519.pdf3.44 MBAdobe PDF