Physical violence detection with movement sensors
Ye, Liang; Wang, Le; Wang, Peng; Ferdinando, Hany; Seppänen, Tapio; Alasaarela, Esko (2018-10-12)
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
© 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.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2020042822800
Tiivistelmä
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%.
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