A wearable system for the affective monitoring of car racing drivers during simulated conditions

A real time, wearable system for remote monitoring of car racing drivers' emotional state is presented. The so-called AUBADE system (standing for AUgmentation system for roBust emotionAl understanding) consists of a wearable device and a centralized unit. The wearable device is responsible to acquire selected biosignals, pre-process them and wirelesses transmit them from the subject-site to the centralized system. The centralized system, which is the main part of the system and carries most of the processing, has a twofold purpose: on the one hand it performs evaluation of the subject's emotional state and on the other hand it projects a generic 3D face model whereat the facial expression of the subject can be viewed. A two stage classification scheme is used. First a decision tree is implemented in order to classify the subject's emotional state as high stress, low stress and valence. Then a Tree Augmented Naive Bayesian classifier (TAN) is used to classify valence as euphoria and dysphoria. The centralized system has been validated using a dataset obtained from ten subjects in simulated racing conditions. The emotional classes identified are high stress, low stress, dysphoria and euphoria. The overall classification rate achieved using tenfold cross-validation is high. AUBADE constitutes the first system which has the feasibility of remote and real time affective assessment in car racing, providing a useful addition to the existing telemetry systems used in the domain.

[1]  Eric Sung,et al.  Anatomically accurate individual face modeling. , 2003, Studies in health technology and informatics.

[2]  Wenqing Zhao,et al.  Transformer Fault Portfolio Diagnosis Based on the Combination of the Multiple Bayesian Classifier and SVM , 2009, 2009 International Conference on Electronic Computer Technology.

[3]  Mohammad Soleymani,et al.  Short-term emotion assessment in a recall paradigm , 2009, Int. J. Hum. Comput. Stud..

[4]  M. Erb,et al.  Brain activity underlying emotional valence and arousal: A response‐related fMRI study , 2004, Human brain mapping.

[5]  Pedro Larrañaga,et al.  Selection of human embryos for transfer by Bayesian classifiers , 2008, Comput. Biol. Medicine.

[6]  Mohan M. Trivedi,et al.  Real-time driver affect analysis and tele-viewing system , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[7]  Christos D. Katsis,et al.  Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[8]  Demetri Terzopoulos,et al.  Physically-based facial modelling, analysis, and animation , 1990, Comput. Animat. Virtual Worlds.

[9]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[10]  Wilfred W. Recker,et al.  Stochastic adaptive control model for traffic signal systems , 2006 .

[11]  Christine L. Lisetti,et al.  Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals , 2004, EURASIP J. Adv. Signal Process..

[12]  Elisabeth André,et al.  Emotion-specific dichotomous classification and feature-level fusion of multichannel biosignals for automatic emotion recognition , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[13]  M. Helander Applicability of drivers' electrodermal response to the design of the traffic environment. , 1978, The Journal of applied psychology.

[14]  Johannes Wagner,et al.  From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[15]  Jason Williams,et al.  Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System , 2004, ADS.

[16]  R. T. Pivik,et al.  Handbook of Psychophysiology: Sleep and Dreaming , 2007 .

[17]  Fred E. Inbau,et al.  Lie Detection And Criminal Interrogation , 2012 .

[18]  W D Fenz,et al.  Gradients of Physiological Arousal in Parachutists as a Function of an Approaching Jump , 1967, Psychosomatic medicine.

[19]  高橋 哲也,et al.  Changes in EEG and autonomic nervous activity during meditation and their association with personality traits , 2004 .

[20]  Carlo Caltagirone,et al.  The recognition of facial emotion expressions in Parkinson's disease , 2008, European Neuropsychopharmacology.

[21]  Christine L. Lisetti,et al.  Emotion recognition from physiological signals using wireless sensors for presence technologies , 2004, Cognition, Technology & Work.

[22]  Lisa Dorn,et al.  Stress, fatigue, health, and risk of road traffic accidents among professional drivers: the contribution of physical inactivity. , 2006, Annual review of public health.

[23]  Wendy S. Ark,et al.  The Emotion Mouse , 1999, HCI.

[24]  Ing-Marie Jonsson,et al.  Automatic recognition of affective cues in the speech of car drivers to allow appropriate responses , 2005, OZCHI.

[25]  H. J. Van Zuylen,et al.  Accurate freeway travel time prediction with state-space neural networks under missing data , 2005 .

[26]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.

[27]  J. R Crawford,et al.  Differential deficits in expression recognition in gene-carriers and patients with Huntington’s disease , 2003, Neuropsychologia.

[28]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[29]  Takehiro Yamakoshi,et al.  A novel physiological index for Driver's Activation State derived from simulated monotonous driving studies , 2009 .

[30]  L. Bretzner,et al.  Towards low-cost systems for measuring visual cues of driver fatigue and inattention in automotive applications , 2005, IEEE International Conference on Vehicular Electronics and Safety, 2005..

[31]  Masaru Kitsuregawa Proceedings : 15th International Conference on Data Engineering, March 23-26, 1999, Sydney, Australia , 1999 .

[32]  A. Damasio,et al.  Basic emotions are associated with distinct patterns of cardiorespiratory activity. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[33]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Byoung-Tak Zhang,et al.  Bayesian Network Classifiers for Gene Expression Analysis , 2003 .

[35]  Sumeet Dua,et al.  Data Shrinking Based Feature Ranking for Protein Classification , 2009, ICISTM.

[36]  J. Ross Quinlan Data Mining from an AI Perspective (Abstract) , 1999, ICDE.

[37]  J. E. Rose,et al.  Autonomic Nervous System Activity Distinguishes Among Emotions , 2009 .

[38]  Jack M. Gorman,et al.  The effect of successful treatment on the emotional and physiological response to carbon dioxide inhalation in patients with panic disorder , 2004, Biological Psychiatry.

[39]  D. I. Fotiadis,et al.  ASSESSMENT OF MUSCLE FATIGUE DURING DRIVING USING SURFACE EMG , 2003 .

[40]  John T. Cacioppo,et al.  Social psychophysiology : a sourcebook , 1983 .

[41]  Diane Nahl,et al.  Road Rage and Aggressive Driving: Steering Clear of Highway Warfare , 2000 .

[42]  Mitsuru Kikuchi,et al.  Changes in EEG and autonomic nervous activity during meditation and their association with personality traits. , 2005, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[43]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[44]  K. H. Kim,et al.  Emotion recognition system using short-term monitoring of physiological signals , 2004, Medical and Biological Engineering and Computing.

[45]  A. W. Young,et al.  Disgust in pre-clinical Huntington's disease: A longitudinal study , 2006, Neuropsychologia.

[46]  H. Li Computer recognition of human emotions , 2001, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489).

[47]  F P McKenna,et al.  The human factor in driving accidents. An overview of approaches and problems. , 1982, Ergonomics.

[48]  Jeremy R. Cooperstock,et al.  Biosignals Analysis and its Application in a Performance Setting - Towards the Development of an Emotional-Imaging Generator , 2008, BIOSIGNALS.