Shi J., Ye L., Ferdinando H., Seppänen T., Alasaarela E. (2020) A School Violence Detection Algorithm Based on a Single MEMS Sensor. In: Liang Q., Liu X., Na Z., Wang W., Mu J., Zhang B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore
A school violence detection algorithm based on a single MEMS sensor
|Author:||Shi, Jifu1; Ye, Liang1,2; Ferdinando, Hany2,3;|
1School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, China
2Optoelectronics and Measurement Techniques Laboratory, Department of Electrical Engineering, University of Oulu, Oulu, Finland
3Department of Electrical Engineering, Petra Christian University, Surabaya, Indonesia
4Department of Computer Science and Engineering, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020042922928
|Publish Date:|| 2020-06-14
School violence has become more and more frequent in today’s school life and caused great harm to the social and educational development in many countries. This paper used a MEMS sensor which is fixed on the waist to collect data and performed feature extraction on the acceleration and gyro data of the sensors. Altogether nine kinds of activities were recorded, including six daily-life kinds and three violence kinds. A filter-based Relief-F feature selection algorithm was used and Radial Basis Function (RBF) neural network classifier was applied on them. The results showed that the algorithm could distinguish physical violence movements from daily-life movements with an accuracy of 90%.
Lecture notes in electrical engineering
|Pages:||474 - 481|
Communications, Signal Processing, and Systems. CSPS 2018
|Host publication editor:||
International Conference in Communications, Signal Processing, and Systems
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
This work 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 those people who have helped with these experiments.
© Springer Nature Singapore Pte Ltd. 2020. This is a post-peer-review, pre-copyedit version of an article published in Communications, Signal Processing, and Systems. CSPS 2018. The final authenticated version is available online at: https://doi.org/10.1007/978-981-13-6508-9_57.