A wearable biosignal system using support vector machines achieved a 79.3% overall classification rate for identifying emotional states in simulated car-racing drivers.
Absolute Event Rate: 79.3% vs 76.7%
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.
Katsis et al. (Wed,) conducted a other in Emotion recognition in car-racing drivers (n=10). Wearable system for emotion recognition (SVM classification) vs. ANFIS classification was evaluated on Overall classification rates for emotional states. A wearable biosignal system using support vector machines achieved a 79.3% overall classification rate for identifying emotional states in simulated car-racing drivers.