A school violence detection algorithm based on a single MEMS sensor

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%.

Shi Jifu, Ye Liang, Ferdinando Hany, Seppänen Tapio, Alasaarela Esko

Publication type:
A4 Article in conference proceedings

Place of publication:
Communications, Signal Processing, and Systems. CSPS 2018

Activity recognition, MEMS accelerometer, RBF neural network, relief-F, school violence


Full citation:
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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