Remarks on emotion recognition from breath gas information

This paper investigates emotion recognition from breath gas information. A breath gas sensing system is designed by using a quartz crystal resonator with a plasma-polymer film as a sensor. For computational experiment of emotion recognition, the machine learning-based approach, such as artificial neural network and support vector machine, is investigated. In emotion recognition experiments by using gathered breath gas data under psychological experiments, the obtained average emotion recognition rate is 70% for two emotions: relaxation / comfortableness (positive emotion) and stress / displeasure (negative emotion). Experimental results show that using breath gas information is feasible and the neural network or support vector machine is suited for this task.