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Physical Violence Detection with Movement Sensors

  • Liang Ye
  • Le Wang
  • Peng Wang
  • Hany Ferdinando
  • Tapio Seppänen
  • Esko Alasaarela
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)

Abstract

With the development of movement sensors, activity recognition becomes more and more popular. Compared with daily-life activity recognition, physical violence detection is more meaningful and valuable. This paper proposes a physical violence detecting method. Movement data of acceleration and gyro are gathered by role playing of physical violence and daily-life activities. Time domain features and frequency domain ones are extracted and filtered to discribe the differences between physical violence and daily-life activities. A specific BPNN trained with the L-M method works as the classifier. Altogether 9 kinds of activities are involved. For 9-class classification, the average recognition accuracy is 67.0%, whereas for 2-class classification, i.e. activities are classified as violence or daily-life activity, the average recognition accuracy reaches 83.7%.

Keywords

Physical violence detection Activity recognition Movement sensor 

Notes

Acknowledgements

This paper was supported by the National Natural Science Foundation of China (61602127), and partly supported by the Directorate General of Higher Education, Indonesia (2142/E4.4/K/2013), and the Finnish Cultural Foundation, North Ostrobothnia Regional Fund.

The authors would like to thank Tuija Huuki, Vappu Sunnari, Seppo Laukka and Antti Siipo from University of Oulu, Finland, teachers Taina Aalto and Pekka Kurttila and principal Maija Laukka from Oulunlahti School, Finland, pupils from 2nd and 6th grades of Oulunlahti School, Tian Han and Zhu Zhang from Harbin University of Science and Technology, China, Yubo Zhang, Jifu Shi and Zhi Xun from Harbin Institute of Technology, China for their assistance to this work.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Liang Ye
    • 1
    • 2
  • Le Wang
    • 1
  • Peng Wang
    • 1
    • 3
  • Hany Ferdinando
    • 2
    • 4
  • Tapio Seppänen
    • 5
  • Esko Alasaarela
    • 2
  1. 1.Communication Research Center, Harbin Institute of TechnologyHarbinChina
  2. 2.Optoelectronics and Measurement Techniques Laboratory, Department of Electrical EngineeringUniversity of OuluOuluFinland
  3. 3.China Electronics Technology Group CorporationNanjingChina
  4. 4.Department of Electrical EngineeringPetra Christian UniversitySurabayaIndonesia
  5. 5.Department of Computer Science and EngineeringUniversity of OuluOuluFinland

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