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

Park, Wan; Wu, Chaozhong; Lin, Yingzi; Ma, Xiaofeng

doi:10.1049/iet-its.2016.0127

Cited by 35 publications

(26 citation statements)

References 25 publications

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“…The proposed SER system can be successfully used in HCI based on speech interaction, as well as integrated in computer-mediated education systems [47], for airplane or car accidents prevention [30,31], diagnosing mental disorders [24][25][26] and physical diseases [27], in call centers to improve the quality of customer service [22,23], and crime investigation departments by analyzing emotional patterns during suspect interrogations to find the perpetrator [28,51].…”

Section: Resultsmentioning

confidence: 99%

“…The proposed architecture can be integrated with computer-mediated education systems and give the tutor the ability to determine the emotions of the students and adapt the content of the course to improve the learning rate [47]. Similarly, the computer system of a car or plane can benefit from this architecture by detecting emotionally tense situations or negative emotions that are affecting the driver or the pilot and take preventive safety measures, such as stopping the car or informing the control tower [30,31]. As the emotional patterns can be used to determine different mental disorders [24][25][26] as well as physical diseases [27], the proposed architecture can be used for real-time monitoring and analysis of these patterns.…”

Section: Discussionmentioning

confidence: 99%

“…We use the most common way of defining emotions, which was first described by Clynes [20] who emphasizes the idea of pure basic sentic states, and later refined by Paul Ekman's research [21], by defining the six basic emotions: Anger, surprise, fear, happiness, sadness, and disgust. Alongside human-computer interfaces (HCI), this architecture can be used for improving the conversation in call center departments by predicting the caller's emotional state and providing advice to agents on how to best approach the caller [22,23]; diagnosing mental disorders or physical diseases based on the emotional patterns displayed by the subject over the course of time (knowing that there are proved correlations between these emotional patterns and several mental disorders [24][25][26] and physical diseases [27]); determining the perpetrator by analyzing suspects' emotions during interrogations in crime investigation departments [28,29]; or preventing plane or car accidents by analyzing driver's or pilot's speech, determining if they are affected by negative emotions, and taking preventive measures in such cases, such as stopping the car or informing the control tower [30,31]. In the following sections, we present the experimental results obtained, as well as the methods used for implementing the proposed architecture.…”

Section: Introductionmentioning

confidence: 99%

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Feedforward Neural Network-Based Architecture for Predicting Emotions from Speech

Gavrilescu

Vizireanu

2019

Data

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We propose a novel feedforward neural network (FFNN)-based speech emotion recognition system built on three layers: A base layer where a set of speech features are evaluated and classified; a middle layer where a speech matrix is built based on the classification scores computed in the base layer; a top layer where an FFNN- and a rule-based classifier are used to analyze the speech matrix and output the predicted emotion. The system offers 80.75% accuracy for predicting the six basic emotions and surpasses other state-of-the-art methods when tested on emotion-stimulated utterances. The method is robust and the fastest in the literature, computing a stable prediction in less than 78 s and proving attractive for replacing questionnaire-based methods and for real-time use. A set of correlations between several speech features (intensity contour, speech rate, pause rate, and short-time energy) and the evaluated emotions is determined, which enhances previous similar studies that have not analyzed these speech features. Using these correlations to improve the system leads to a 6% increase in accuracy. The proposed system can be used to improve human–computer interfaces, in computer-mediated education systems, for accident prevention, and for predicting mental disorders and physical diseases.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feedforward Neural Network-Based Architecture for Predicting Emotions from Speech

Gavrilescu

Vizireanu

2019

Data

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“…When the sum of

and

was 3 or 4, the value of

was assigned to 3, indicating that the driver’s demand satisfaction was over met. In the car-following theory, the distance between vehicles is often regarded as an index to measure the safety risk [ 66 ]. In this study, the

was defined as risk degree (

) and the

was assigned to 3, 2, and 1, respectively, when the

was

, and

, respectively.…”

Section: Discussionmentioning

confidence: 99%

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

Liu

Wang

2020

IJERPH

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Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system.

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“…Second, after wearing and configuring all the apparatus, especially the physiological signals acquisition system, every subject did ten-minute driving practice to adapt to the apparatus as well as the test vehicle. Third, when the formal test began, every two minutes during the experimental process or any moment an anger elicitation event occurred, the subject was surveyed by the observer through a very simple question(the question is too simple to interference the subject [37]), "How do you feel in the last 2 minutes" or " How do you feel just now?" And they had to respond to evaluate which emotion he felt the most and how intensely through the seven-point scale.…”

Section: Experiments Designmentioning

confidence: 99%

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

Park

Lin

et al. 2019

Discrete Dynamics in Nature and Society

Self Cite

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

Cited by 35 publications

References 25 publications

Feedforward Neural Network-Based Architecture for Predicting Emotions from Speech

Feedforward Neural Network-Based Architecture for Predicting Emotions from Speech

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

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

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