School violence detection based on multi-sensor fusion and improved relief-F algorithms

Liang Ye; Jifu Shi; Hany Ferdinando; Tapio Seppänen; Esko Alasaarela

doi:10.1007/978-3-030-22971-9_22

School violence detection based on multi-sensor fusion and improved relief-F algorithms

Liang Ye^*
, Jifu Shi
, Hany Ferdinando
, Tapio Seppänen
, Esko Alasaarela

^*Corresponding author for this work

School of Electronics and Information Engineering

University of Oulu
Harbin Institute of Technology
Petra Christian University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

School bullying is a common social problem around the world, and school violence is considered to be the most harmful form of school bullying. This paper proposes a school violence detecting method based on multi-sensor fusion and improved Relief-F algorithms. Data are gathered with two movement sensors by role playing of school violence and daily-life activities. Altogether 9 kinds of activities are recorded. Time domain features and frequency domain features are extracted and filtered by an improved Relief-F algorithm. Then the authors build a two-level classifier. The first level is a Decision Tree classifier which separates the activity of jump from the others, and the second level is a Radial Basis Function neural network which classifies the remainder 8 kinds of activities. Finally a decision layer fusion algorithm combines the recognition results of the two sensors together. The average recognition accuracy of school violence reaches 84.4%, and that of daily-life reaches 97.3%.

Original language	English
Title of host publication	Artificial Intelligence for Communications and Networks - 1st EAI International Conference, AICON 2019, Proceedings
Editors	Shuai Han, Liang Ye, Weixiao Meng
Publisher	Springer Verlag
Pages	261-269
Number of pages	9
ISBN (Print)	9783030229702
DOIs	https://doi.org/10.1007/978-3-030-22971-9_22
State	Published - 2019
Event	1st EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019 - Harbin, China Duration: 25 May 2019 → 26 May 2019

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	287
ISSN (Print)	1867-8211

Conference

Conference	1st EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019
Country/Territory	China
City	Harbin
Period	25/05/19 → 26/05/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Activity recognition
Artificial intelligence
Improved Relief-F
Pattern recognition
School violence

Access to Document

10.1007/978-3-030-22971-9_22

Cite this

Ye, L., Shi, J., Ferdinando, H., Seppänen, T., & Alasaarela, E. (2019). School violence detection based on multi-sensor fusion and improved relief-F algorithms. In S. Han, L. Ye, & W. Meng (Eds.), Artificial Intelligence for Communications and Networks - 1st EAI International Conference, AICON 2019, Proceedings (pp. 261-269). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 287). Springer Verlag. https://doi.org/10.1007/978-3-030-22971-9_22

Ye, Liang ; Shi, Jifu ; Ferdinando, Hany et al. / School violence detection based on multi-sensor fusion and improved relief-F algorithms. Artificial Intelligence for Communications and Networks - 1st EAI International Conference, AICON 2019, Proceedings. editor / Shuai Han ; Liang Ye ; Weixiao Meng. Springer Verlag, 2019. pp. 261-269 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{c5d8b291b9e64cc0a3bd9fbe45a21220,

title = "School violence detection based on multi-sensor fusion and improved relief-F algorithms",

abstract = "School bullying is a common social problem around the world, and school violence is considered to be the most harmful form of school bullying. This paper proposes a school violence detecting method based on multi-sensor fusion and improved Relief-F algorithms. Data are gathered with two movement sensors by role playing of school violence and daily-life activities. Altogether 9 kinds of activities are recorded. Time domain features and frequency domain features are extracted and filtered by an improved Relief-F algorithm. Then the authors build a two-level classifier. The first level is a Decision Tree classifier which separates the activity of jump from the others, and the second level is a Radial Basis Function neural network which classifies the remainder 8 kinds of activities. Finally a decision layer fusion algorithm combines the recognition results of the two sensors together. The average recognition accuracy of school violence reaches 84.4\%, and that of daily-life reaches 97.3\%.",

keywords = "Activity recognition, Artificial intelligence, Improved Relief-F, Pattern recognition, School violence",

author = "Liang Ye and Jifu Shi and Hany Ferdinando and Tapio Sepp{\"a}nen and Esko Alasaarela",

note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019 Published by Springer Nature Switzerland AG 2019. All Rights Reserved.; 1st EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019 ; Conference date: 25-05-2019 Through 26-05-2019",

year = "2019",

doi = "10.1007/978-3-030-22971-9\_22",

language = "英语",

isbn = "9783030229702",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Verlag",

pages = "261--269",

editor = "Shuai Han and Liang Ye and Weixiao Meng",

booktitle = "Artificial Intelligence for Communications and Networks - 1st EAI International Conference, AICON 2019, Proceedings",

address = "德国",

}

Ye, L, Shi, J, Ferdinando, H, Seppänen, T & Alasaarela, E 2019, School violence detection based on multi-sensor fusion and improved relief-F algorithms. in S Han, L Ye & W Meng (eds), Artificial Intelligence for Communications and Networks - 1st EAI International Conference, AICON 2019, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 287, Springer Verlag, pp. 261-269, 1st EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019, Harbin, China, 25/05/19. https://doi.org/10.1007/978-3-030-22971-9_22

School violence detection based on multi-sensor fusion and improved relief-F algorithms. / Ye, Liang; Shi, Jifu; Ferdinando, Hany et al.
Artificial Intelligence for Communications and Networks - 1st EAI International Conference, AICON 2019, Proceedings. ed. / Shuai Han; Liang Ye; Weixiao Meng. Springer Verlag, 2019. p. 261-269 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 287).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - School violence detection based on multi-sensor fusion and improved relief-F algorithms

AU - Ye, Liang

AU - Shi, Jifu

AU - Ferdinando, Hany

AU - Seppänen, Tapio

AU - Alasaarela, Esko

PY - 2019

Y1 - 2019

N2 - School bullying is a common social problem around the world, and school violence is considered to be the most harmful form of school bullying. This paper proposes a school violence detecting method based on multi-sensor fusion and improved Relief-F algorithms. Data are gathered with two movement sensors by role playing of school violence and daily-life activities. Altogether 9 kinds of activities are recorded. Time domain features and frequency domain features are extracted and filtered by an improved Relief-F algorithm. Then the authors build a two-level classifier. The first level is a Decision Tree classifier which separates the activity of jump from the others, and the second level is a Radial Basis Function neural network which classifies the remainder 8 kinds of activities. Finally a decision layer fusion algorithm combines the recognition results of the two sensors together. The average recognition accuracy of school violence reaches 84.4%, and that of daily-life reaches 97.3%.

AB - School bullying is a common social problem around the world, and school violence is considered to be the most harmful form of school bullying. This paper proposes a school violence detecting method based on multi-sensor fusion and improved Relief-F algorithms. Data are gathered with two movement sensors by role playing of school violence and daily-life activities. Altogether 9 kinds of activities are recorded. Time domain features and frequency domain features are extracted and filtered by an improved Relief-F algorithm. Then the authors build a two-level classifier. The first level is a Decision Tree classifier which separates the activity of jump from the others, and the second level is a Radial Basis Function neural network which classifies the remainder 8 kinds of activities. Finally a decision layer fusion algorithm combines the recognition results of the two sensors together. The average recognition accuracy of school violence reaches 84.4%, and that of daily-life reaches 97.3%.

KW - Activity recognition

KW - Artificial intelligence

KW - Improved Relief-F

KW - Pattern recognition

KW - School violence

UR - https://www.scopus.com/pages/publications/85069163667

U2 - 10.1007/978-3-030-22971-9_22

DO - 10.1007/978-3-030-22971-9_22

M3 - 会议稿件

AN - SCOPUS:85069163667

SN - 9783030229702

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 261

EP - 269

BT - Artificial Intelligence for Communications and Networks - 1st EAI International Conference, AICON 2019, Proceedings

A2 - Han, Shuai

A2 - Ye, Liang

A2 - Meng, Weixiao

PB - Springer Verlag

T2 - 1st EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019

Y2 - 25 May 2019 through 26 May 2019

ER -

Ye L, Shi J, Ferdinando H, Seppänen T, Alasaarela E. School violence detection based on multi-sensor fusion and improved relief-F algorithms. In Han S, Ye L, Meng W, editors, Artificial Intelligence for Communications and Networks - 1st EAI International Conference, AICON 2019, Proceedings. Springer Verlag. 2019. p. 261-269. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-22971-9_22

School violence detection based on multi-sensor fusion and improved relief-F algorithms

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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