Dynamic Emotion Estimation Based on Physiological Signals

Abstract

Affective computing is becoming more and more popular, and the need to find a user-friendly and reliable method of estimating people’s emotions, in their everyday life, is growing. Traditional methods have reached their limits, and this thesis presents a new system of emotion recognition, though physiological signals. With a user-friendly, wearable device, the system can be deployed in a number of fields. A model for our emotion classification is presented and includes the following emotions: cheerfulness, sadness, erotic, horror, and neutral. An experiment of emotion elicitation is also described in this work. Three analysis models applied in our system in order to recognize emotions, including nearest neighbor, discriminant analysis, and multi-layer perception, are discussed in detail. The final test results show that the system has the average recognition rates of 40%, 55.7%, and 77.34% for nearest neighbor, discriminant analysis, and multi-layer perception respectively.