Comparison of ROC curves for landmine detection by holographic radar with ROC data from other methods

et al. 2016

Information

Driving anger, called "road rage", has become increasingly common nowadays, affecting road safety. A few researches focused on how to identify driving anger, however, there is still a gap in driving anger grading, especially in real traffic environment, which is beneficial to take corresponding intervening measures according to different anger intensity. This study proposes a method for discriminating driving anger states with different intensity based on Electroencephalogram (EEG) spectral features. First, thirty drivers were recruited to conduct on-road experiments on a busy route in Wuhan, China where anger could be inducted by various road events, e.g., vehicles weaving/cutting in line, jaywalking/cyclist crossing, traffic congestion and waiting red light if they want to complete the experiments ahead of basic time for extra paid. Subsequently, significance analysis was used to select relative energy spectrum of β band (β%) and relative energy spectrum of θ band (θ%) for discriminating the different driving anger states. Finally, according to receiver operating characteristic (ROC) curve analysis, the optimal thresholds (best cut-off points) of β% and θ% for identifying none anger state (i.e., neutral) were determined to be 0.2183 ≤ θ% < 1, 0 < β% < 0.2586; low anger state is 0.1539 ≤ θ% < 0.2183, 0.2586 ≤ β% < 0.3269; moderate anger state is 0.1216 ≤ θ% < 0.1539, 0.3269 ≤ β% < 0.3674; high anger state is 0 < θ% < 0.1216, 0.3674 ≤ β% < 1. Moreover, the discrimination performances of verification indicate that, the overall accuracy (Acc) of the optimal thresholds of β% for discriminating the four driving anger states is 80.21%, while 75.20% for that of θ%. The results can provide theoretical foundation for developing driving anger detection or warning devices based on the relevant optimal thresholds.

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Section: Methods Of Roc Curve Analysismentioning

confidence: 99%

“…Here, the optimal thresholds of driving anger states with different intensity based on θ% and β% will be determined by ROC curve analysis. According to [31][32][33][34], three important principles of ROC curve analysis method for determining driving anger states are listed as follows.…”

Section: Methods Of Roc Curve Analysismentioning

confidence: 99%

Optimal Threshold Determination for Discriminating Driving Anger Intensity Based on EEG Wavelet Features and ROC Curve Analysis

et al. 2016

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“…In order to evaluate the classification performance of LSSVM model proposed, an evaluation method of receiver operating characteristic (ROC) curve is introduced in this study. The evaluation method, deriving from detection theory for electrical signals, has been extensively applied in many fields such as medical diagnosis, human decision-making, military monitoring and industrial quality control [50][51][52]. As we know, identification evidence of most classifier is based on its output, which reflects the probability of a sample recognized as positive or negative ones.…”

Section: Construction Principle Of Roc Curvementioning

confidence: 99%

Driving Anger States Detection Based on Incremental Association Markov Blanket and Least Square Support Vector Machine

Discrete Dynamics in Nature and Society

et al. 2019

Driving anger, known as “road rage”, has gradually become a serious traffic psychology issue. Although driving anger identification is solved in some studies, there is still a gap in driving anger grading which is helpful to take different intervening measures for different anger intensity, especially in real traffic environment. The main objectives of this study are: (1) explore a novel driving anger induction method based on various elicitation events, e.g., traffic congestion, vehicles weaving/cutting in line, jaywalking and red light waiting in real traffic environment; (2) apply incremental association Markov blanket (IAMB) algorithm to select typical features related to driving anger states; (3) employ least square support vector machine (LSSVM) to identify different driving anger states based on the selected features. Thirty private car drivers were enrolled to perform field experiments on a busy route selected in Wuhan, China, where drivers’ anger could be induced by the elicitation events within limited time. Meanwhile, three types of data sets including driver physiology, driving behaviors and vehicle motions, were collected by multiple sensors. The results indicate that 13 selected features including skin conductance, relative energy spectrum of β band of electroencephalogram, standard deviation (SD) of pedaling speed of gas pedal, SD of steering wheel angle rate, vehicle speed, SD of speed, SD of forward acceleration and SD of lateral acceleration have significant impact on driving anger states. The IAMB-LSSVM model achieves an accuracy with 82.20% which is 2.03%, 3.15%, 4.34%, 7.84% and 8.36% higher than IAMB using C4.5, NBC, SVM, KNN and BPNN, respectively. The results are beneficial to design driving anger detecting or intervening devices in intelligent human-machine systems.

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“…Recently, ROC curve analysis method originated from electrical signal detection theory has been widely used in many fields including medical diagnosis, industrial quality control, human decision‐making and military monitoring [25–27]. It is a quantitative approach for accurate judgment of two confused states by determining the optimal discriminant threshold.…”

Section: Driving Anger Identification Modelmentioning

confidence: 99%

On‐road experimental study on driving anger identification model based on physiological features by ROC curve analysis

IET intell. transp. syst

et al. 2017