A school violence detection algorithm based on a single MEMS sensor

Shi, Jifu; Ye, Liang; Ferdinando, Hany; Seppänen, Tapio; Alasaarela, Esko (2019-06-14)
 
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https://doi.org/10.1007/978-981-13-6508-9_57

Shi, Jifu
Ye, Liang
Ferdinando, Hany
Seppänen, Tapio
Alasaarela, Esko
Springer Nature
14.06.2019

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

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© 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.
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doi:https://doi.org/10.1007/978-981-13-6508-9_57
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https://urn.fi/URN:NBN:fi-fe2020042922928
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Abstract

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%.

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