Ye L., Wang L., Wang P., Ferdinando H., Seppänen T., Alasaarela E. (2018) Physical Violence Detection with Movement Sensors. In: Meng L., Zhang Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham
Physical violence detection with movement sensors
|Author:||Ye, Liang1,2; Wang, Le1; Wang, Peng1,3;|
1Communication Research Center, Harbin Institute of Technology, Harbin 150080, China
2Optoelectronics and Measurement Techniques Laboratory, Department of Electrical Engineering, University of Oulu, Oulu 90570, Finland
3China Electronics Technology Group Corporation, Nanjing 210012, China
4Department of Electrical Engineering, Petra Christian University, Surabaya 60236, Indonesia
5Department of Computer Science and Engineering, University of Oulu, Oulu 90570, Finland
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020042822800
|Publish Date:|| 2020-04-28
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%.
Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
|Pages:||190 - 197|
Machine Learning and Intelligent Communications Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings
|Host publication editor:||
International Conference on Machine Learning and Intelligent Communications
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
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.
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. This is a post-peer-review, pre-copyedit version of an article published in Machine Learning and Intelligent Communications Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-00557-3_20.