Study on Breath Gas Information Sensing and Its Application to Man-Machine Interface

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This paper proposes a smart gas sensing system to achieve emotion recognition using breath gas information. A breath gas sensing system is designed by using s quartz crystal resonator with a plasma-polymer film as a sensor. For computational experiment of emotion recognition, the machine learning-based approaches, such as neural network (NN) and support vector machine (SVM), are investigated. In emotion recognition experiments by using gathered breath gas data under psychological experiments, the obtained maximum average emotion recognition rates are 75% using ANN and 83% using SVM for three emotions: pleasure, displeasure, and no emotion. In emotion recognition experiments by using a dental rinse as a stimulus for exciting emotion, the obtained maximum average emotion recognition rate is 47.5% using ANN for two emotions: comfortableness and no emotion. Experimental results show that using breath gas information is feasible and the machine learning-based approach is well suited for this task.