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

Transportation Research Part C: Emerging Technologies

Volumn 19, Issue 3, 2011, Pages 541-551

  Katsis, C D ^a   Goletsis, Y ^a   Rigas, G ^a   Fotiadis, D I ^a  

^a UNIVERSITY OF IOANNINA   (Greece)

Author keywords

Biosignal processing; C4.5; Car driver affective monitoring; Emotion recognition; Tree Augmented Bayesian classifier; Wearable devices

Indexed keywords

3D MODELING; DECISION TREES; RACING AUTOMOBILES; REAL TIME SYSTEMS; WEARABLE TECHNOLOGY;

BAYESIAN CLASSIFIER; BIO-SIGNAL PROCESSING; C4.5; CAR DRIVER; EMOTION RECOGNITION; WEARABLE DEVICES;

MONITORING;

CAR USE; COGNITION; MONITORING SYSTEM; SIGNALING; TELEMETRY; THREE-DIMENSIONAL MODELING;

EID: 79951768783     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2010.09.004     Document Type: Article

References (46)

1. Anders S., Lotze M., Erb M., Grodd W., Birbaumer N. Brain activity underlying emotional valence and arousal: a response-related fMRI study. Human Brain Mapping 2004, 23:200-209.
2. Ark, W.S., Dryer, D.C., Lu, D.J., 1999. The emotion mouse. In: Proceedings of HCI International (the 8th International Conference on Human-Computer Interaction) on Human-Computer Interaction: Ergonomics and User Interfaces, Munich, Germany, 24-29 September, 1999, pp. 818-823.
3. Assogna F., Pontieri F.E., Caltagirone C., Spalletta G. The recognition of facial emotion expressions in Parkinson's disease. European Neuropsychopharmacology 2008, 18(11):835-848.
4. Benovoy, M., Deitcher, J., Cooperstock, J.R., 2008. Biosignals analysis and its application in a performance setting: towards the development of an emotional-imaging generator. In: IEEE International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS) Portugal, January 2008.
5. Breiman L., Friedman V.R., Olsen A., Stone C.J. Classification and Regression Trees 1984, Wadsworth & Brooks, Pacific Grove, CA.
6. Bretzner, L., Krantz, M., 2005. Towards low-cost systems for measuring visual cues of driver fatigue and inattention in automotive applications. In: Proceedings of the 2005 IEEE International Conference on Vehicular Electronics and Safety, Xi'an, China, October 2005, pp. 161-164.
7. Chanel G., Kierkels J.M., Soleymani M., Pun T. Short-term emotion assessment in a recall paradigm. International Journal of Human-Computer Studies 2009, 67:607-627.
8. Dawson M.E., Schell A.M., Filion D.L. Handbook of Psychophysiology 2000, Cambridge University Press, New York.
9. Dua S., Saini S. Data shrinking based feature ranking for protein classification. Communications in Computer and Information Science 2009, 31:54-63.
10. Ekman P., Levenson R.W., Friesen W.V. Autonomic nervous system activity distinguishes among emotions. Science 1983, 221:1208-1210.
11. Fenz W.D., Epstein S. Gradients of physiological arousal in parachutists as a function of an approaching jump. Psychosomatic Medicine 1967, 29(1):33-51.
12. Flidlund, A.J., Izard, E.Z., 1983. Electromyographic studies of facial expressions of emotions and patterns of emotions. In: Social Psychophysiology: A Sourcebook. Guilford Press, New York, pp. 243-286.
13. Fred E. Lie Detection and Criminal Interrogation 1948, The Williams & Wilkins Company.
14. Friedman N., Geiger D., Goldszmidt M. Bayessian network classifiers. Machine Learning 1997, 29(2-3):131-163.
15. Gorman J.M., Martinez J., Coplan J.D., Kent J., Kleber M. The effect of successful treatment on the emotional and physiological response to carbon dioxide inhalation in patients with panic disorder. Biological Psychiatry 2004, 56(11):862-867.
16. Haag A., Gornzy S., Schaich P., Williams J. Emotion recognition using bio-sensors: first steps towards an automatic system. LNAI 2004, 36-48.
17. Healey J., Picard R.W. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems 2005, 6:156-166.
18. Helander M. Applicability of drivers' electrodermal response to the design of the traffic environment. Journal of Applied Psychology 1978, 63(4):481-488.
19. James, L., Nahl, D., 2000. Road Rage and Aggressive Driving: Steering Clear of Highway Warfare, NY.
20. Jones, C.M., Jonsson, I.M., 2005. Automatic recognition of affective cues in the speech of car drivers to allow appropriate responses. In: Proceedings of the 17th Australia conference on Computer-Human Interaction, Canberra, Australia, 2005, pp. 1-10.
21. Katsis, C.D., Ntouvas, N.E., Bafas, C.G., Fotiadis, D.I., 2004. Assessment of muscle fatigue during driving using surface EMG. In: Proceedings of the 2nd IASTED International Conference on Biomedical Engineering, Innsbruck, Austria, 2004.
22. Katsis C.D., Katertsidis N., Ganiatsas G., Fotiadis D.I. Toward emotion recognition in car-racing drivers: a biosignal processing approach. IEEE Transactions on Systems Man and Cybernetics Part A: Systems and Humans 2008, 38(3):502-512.
23. Kim, J., Andre, E., 2008. Emotion-specific dichotomous classification and feature-level fusion of multichannel biosignals for automatic emotion recognition. In: Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Seoul, 20-22 Aug, pp. 114-119.
24. Kim K.H., Bang S.W., Kim S.R. Emotion recognition system using short-term monitoring of physiological signals. Medical and Biological Engineering and Computing 2004, 42(3):419-427.
25. Li, H., 2001. Computer recognition of human emotions. In: Proceedings of 2001 International Symposium on Video and Speech Processing, Hong Kong, China 2001, pp. 490-493.
26. Lint J.W.C., Hoogendoorn S.P., Zuylen H.J. Accurate freeway travel time prediction with state-space neural networks under missing data. Transportation Research Part C: Emerging Technologies 2005, 13(6):347-369.
27. Lisetti C.L., Nasoz F. Using noninvasive wearable computers to recognize human emotions from physiological signals. Journal on Applied Signal Processing 2004, 11:1672-1687.
28. McCall, J.C., Mallick, S.P., Trivedi, M.M., 2003. Real-time driver affect analysis and tele-viewing system. In: Proceedings of 2003 Intelligent Vehicles Symposium, IEEE IV2003, Ohio USA, 9-11 June, pp. 372-377.
29. McKenna F. The human factor in driving accidents. An overview of approaches and problems. Ergonomics 1982, 25(10):867-877.
30. Milders M., Crawford J.R., Lamb A., Simpson S.A. Differential deficits in expression recognition in gene-carriers and patients with Huntington's disease. Neuropsychologia 2003, 41(11):1484-1492.
31. Morales D.A., Bengoetxea E., Larrañaga P. Selection of human embryos for transfer by Bayesian classifiers. Computers in Biology and Medicine 2008, 38(11-12):177-186.
32. Nasoz, F., Alvarez, K., Lisetti, C., 2004. Emotion recognition from physiological signals using wireless sensors for presence technologies. Cognition Technology and Work 6, 4-14.
33. Paliouras, G., Karkaletsis, V., Spyropoulos, C.D., 2001. Machine Learning and Its Applications: Advanced Lectures, Springer Lecture Notes In Artificial Intelligence, vol. 2001.
34. Pedro D., Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 1997, 29:103-137.
35. Picard R.W., Vyzas E., Healey J. Toward machine emotional intelligence: analysis of affective physiological state pattern analysis and machine intelligence. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001, 23:1175-1191.
36. Quinlan, R., 1999. Data mining from an Al perspective. In: Proceedings of the 15th International Conference on Data Engineering, Sidney, Australia, 23-26 March, pp. 186-186.
37. Rainville P., Bechara A., Naqvi N., Damasio A.R. Basic emotions are associated with distinct patterns of cardiorespiratory activity. International Journal of Psychophysiology 2006, 61(1):5-18.
38. Sprengelmeyer R., Schroeder U., Young A.W., Epplen J.T. Disgust in pre-clinical Huntington's disease: a longitudinal study. Neuropsychologia 2006, 44(4):518-533.
39. Takahashi T., Murata T., Hamada T., Omori M., Kosaka H., Kikuchi M., Yoshida H., Wada Y.L. Changes in EEG and autonomic nervous activity during meditation and their association with personality traits. International Journal of Psychophysiology 2005, 55(2):199-207.
40. Taylor A., Dorn L. Stress, fatigue, health, and risk of road traffic accidents among professional drivers: the contribution of physical inactivity. Annual Review of Public Health 2006, 27:371-391.
41. Terzopoulos D., Watersy K. Physically-based facial modeling, analysis and animation. Journal of Visualization and Computer Animation 1990, 1(2):73-80.
42. Wagner, J., Kim, J., André, E., 2005. From physiological signals to emotions: implementing and comparing selected methods for features extraction and classification. In: IEEE International Conference on Multimedia & Expo. July, Amsterdam, Netherlands.
43. Wu, Z., Zhang, B., Zhu, Y., Zhao, W., Zhou, Y., 2009. Transformer fault portfolio diagnosis based on the combination of the multiple bayesian classifier and SVM." In: Proceedings of the International Conference on Electronic Computer Technology, Macau, China, 20-22 February, pp. 379-382.
44. Yamakoshi T., Rolfe P., Yamakoshi Y., Hirose H. A novel physiological index for Driver's activation state derived from simulated monotonous driving studies. Transportation Research Part C: Emerging Technologies 2009, 17(1):69-80.
45. Yu X.H., Recker W.W. Stochastic adaptive control model for traffic signal systems. Transportation Research Part C: Emerging Technologies 2006, (4):263-282.
46. Zhang Y., Prakash E.C., Sung E. Anatomically accurate individual face modeling. Studies in Health Technology and Informatics 2003, 94:407-409.