Abstract
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 a 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. Experimental results show that using breath gas information is feasible and the machine learning-based approach is well suited for this task.
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Takahashi, K., Sugimoto, I. (2008). Remarks on Emotion Recognition Using Breath Gas Sensing System. In: Mukhopadhyay, S.C., Gupta, G.S. (eds) Smart Sensors and Sensing Technology. Lecture Notes Electrical Engineering, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79590-2_4
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DOI: https://doi.org/10.1007/978-3-540-79590-2_4
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-540-79590-2
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