University of Oulu

Ye, L., Wang, P., Wang, L., Ferdinando, H., Seppänen, T., & Alasaarela, E. (2018). A Combined Motion-Audio School Bullying Detection Algorithm. International Journal of Pattern Recognition and Artificial Intelligence, 32(12), 1850046. https://doi.org/10.1142/s0218001418500465

A combined motion-audio school bullying detection algorithm

Saved in:
Author: Ye, Liang1,2; Wang, Peng3; Wang, Le1;
Organizations: 1Department of Information and Communication Engineering, Harbin Institute of Technology No.2 Yikuang Street, Harbin 150080, China
2Health and Wellness Measurement research group, OPEM unit, University of Oulu Pentti Kaiteran katu 1, Oulu 90014, Finland
3China Electronics Technology Group Corporation No.8 Guorui Road, Nanjing 210012, Chin
4Department of Electrical Engineering, Petra Christian University Siwalankerto 121 - 131, Surabaya 60236, Indonesia
5Physiological Signal Analysis Team, University of Oulu Pentti Kaiteran katu 1, Oulu 90014, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020042822798
Language: English
Published: World Scientific, 2018
Publish Date: 2020-04-28
Description:

Abstract

School bullying is a common social problem, which affects children both mentally and physically, making the prevention of bullying a timeless topic all over the world. This paper proposes a method for detecting bullying in school based on activity recognition and speech emotion recognition. In this method, motion and voice data are gathered by movement sensors and a microphone, followed by extraction of a set of motion and audio features to distinguish bullying incidents from daily life events. Among extracted motion features are both time-domain and frequency-domain features, while audio features are computed with classical MFCCs. Feature selection is implemented using the wrapper approach. At the next stage, these motion and audio features are merged to form combined feature vectors for classification, and LDA is used for further dimension reduction. A BPNN is trained to recognize bullying activities and distinguish them from normal daily life activities. The authors also propose an action transition detection method to reduce computational complexity for practical use. Thus, the bullying detection algorithm will only run, when an action transition event has been detected. Simulation results show that the combined motion-audio feature vector outperforms separate motion features and acoustic features, achieving an accuracy of 82.4% and a precision of 92.2%. Moreover, with the action transition method, the computation cost can be reduced by half.

see all

Series: International journal of pattern recognition and artificial intelligence
ISSN: 0218-0014
ISSN-E: 0218-0014
ISSN-L: 0218-0014
Volume: 32
Issue: 12
Article number: 1850046
DOI: 10.1142/S0218001418500465
OADOI: https://oadoi.org/10.1142/S0218001418500465
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work was supported by the National Natural Science Foundation of China (61602127), with significant contributions by the Directorate General of Higher Education, Indonesia (2142/E4.4/K/2013) and by the North Ostrobothnia Regional Fund of the Finnish Cultural Foundation.
Copyright information: Preprint of an article published in International Journal of Pattern Recognition and Artificial Intelligence, Volume: 32, Issue: 12, 2018, Article Number: 1850046 https://doi.org/10.1142/s0218001418500465 © World Scientific Publishing Company] https://www.worldscientific.com/worldscinet/ijprai.