Classification of Emotional State in Group Discussion by Sensor Data

Abstract

If we can recognize emotions in real time from people's biometric data, it will be possible to respond and speak according to their emotions. In this study, we conducted group discussion experiments with 15 university students participating in eight sessions. During these experiments, we obtained the participants' body movement data and emotional annotation data by subjective evaluation. A machine learning method was used to recognize emotions using acceleration and gyro sensor data. As a result of initial examination by Random Forest, we obtained a discrimination rate of more than 70% for pleasant and unpleasant states.