Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach
In this paper, we present a methodology and a wearable system for the evaluation of the emotional states of car-racing drivers. The proposed approach performs an assessment of the emotional states using facial electromyograms, electrocardiogram, respiration, and electrodermal activity. The system co...
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| Published in | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 38; no. 3; pp. 502 - 512 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.05.2008
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| Subjects | |
| Online Access | Get full text |
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| Abstract | In this paper, we present a methodology and a wearable system for the evaluation of the emotional states of car-racing drivers. The proposed approach performs an assessment of the emotional states using facial electromyograms, electrocardiogram, respiration, and electrodermal activity. The system consists of the following: 1) the multisensorial wearable module; 2) the centralized computing module; and 3) the system's interface. The system has been preliminary validated by using data obtained from ten subjects in simulated racing conditions. The emotional classes identified are high stress, low stress, disappointment, and euphoria. Support vector machines (SVMs) and adaptive neuro-fuzzy inference system (ANFIS) have been used for the classification. The overall classification rates achieved by using tenfold cross validation are 79.3% and 76.7% for the SVM and the ANFIS, respectively. |
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| AbstractList | In this paper, we present a methodology and a wearable system for the evaluation of the emotional states of car-racing drivers. The proposed approach performs an assessment of the emotional states using facial electromyograms, electrocardiogram, respiration, and electrodermal activity. The system consists of the following: 1) the multisensorial wearable module; 2) the centralized computing module; and 3) the system's interface. The system has been preliminary validated by using data obtained from ten subjects in simulated racing conditions. The emotional classes identified are high stress, low stress, disappointment, and euphoria. Support vector machines (SVMs) and adaptive neuro-fuzzy inference system (ANFIS) have been used for the classification. The overall classification rates achieved by using tenfold cross validation are 79.3% and 76.7% for the SVM and the ANFIS, respectively. |
| Author | Katertsidis, N. Ganiatsas, G. Fotiadis, D.I. Katsis, C.D. |
| Author_xml | – sequence: 1 givenname: C.D. surname: Katsis fullname: Katsis, C.D. organization: Univ. of Ioannina, Ioannina – sequence: 2 givenname: N. surname: Katertsidis fullname: Katertsidis, N. – sequence: 3 givenname: G. surname: Ganiatsas fullname: Ganiatsas, G. – sequence: 4 givenname: D.I. surname: Fotiadis fullname: Fotiadis, D.I. |
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| SubjectTerms | Adaptive neuro-fuzzy inference system (ANFIS) Assessments biosignal processing Classification Computer interfaces Computer science Drivers Emotion recognition Inference Information systems Intelligent systems Modules Psychology Stress Stresses Support vector machine classification Support vector machines support vector machines (SVMs) Wearable Wearable computers wearable system |
| Title | Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach |
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