Campus bullying detection based on motion recognition and speech emotion recognition

Abstract

In many areas, school bullying incidents often occur. Such incidents not only damage the learning environment of the school but also cause great harm to the students who are bullied. The occurrence of the campus bullying incident is generally hidden and not easy to be discovered by teachers or parents in time. Aiming at the problem that the campus bullying incident needs to be discovered in time, a campus bullying detection method based on motion recognition and speech emotion recognition is proposed. The electronic device such as smart phone and smart watch worn by people are used to collect the data of human motion and voice in real time. Thus, it is possible to detect in time whether the wearer is being bullied. In this paper, six characteristics of human motion data and MFCC features of speech data are extracted. Then the PCA algorithm is used to reduce the dimension of the obtained feature matrix. Finally, the KNN algorithm is used to form the motion and speech recognition network. After cross-validation, the average recognition rate of the bullying movement and speech emotion of the system in this paper are 77.8% and 81.4%, respectively. The experimental results show that the campus bullying detection method based on KNN algorithm has obtained a good recognition rate.