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School bullying is a common social problem, and school violence is considered to be the most harmful form of school bullying. Fortunately, with the development of movement sensors and pattern recognition techniques, it is possible to detect school violence with artificial intelligence. This paper proposes a school violence detecting method based on improved Relief-F and Dempster-Shafe (D-S) algorithms. Two movement sensors are fixed on the object’s waist and leg, respectively, to gather acceleration and gyro data. Altogether nine kinds of activities are gathered, including three kinds of school violence and six kinds of daily-life activities. After wavelet filtering, 39 time-domain features and 12 frequency-domain features are extracted. To reduce computational cost, this paper proposes an improved Relief-F algorithm which selects features according to classification contribution and correlation. By drawing boxplots of the selected features, the authors find that the frequency-domain energy of the y-axis of acceleration can distinguish jumping from other activities. Therefore, the authors build a two-layer classifier. The first layer is a decision tree which separates jumping from other activities, and the second layer is a Radial Basis Function (RBF) neutral network which classifies the remainder eight kinds of activities. Since the two movement sensors work independently, this paper proposes an improved D-S algorithm for decision layer fusion. The improved D-S algorithm designs a new probability distribution function on the evidence model and builds a new fusion rule, which solves the problem of fusion collision. According to the simulation results, the proposed method has increased the recognition accuracy compared with the authors’ previous work. 89.6% of school violence and 95.1% of daily-life activities were correctly recognized. The accuracy reached 93.6% and the precision reached 87.8%, which were 29.9% and 2.7% higher than the authors’ previous work, respectively.

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%.

School bullying is a common social problem around the world, and school violence is considered to be the most harmful form of school bullying. This paper proposes a school violence detecting method based on multi-sensor fusion and improved Relief-F algorithms. Data are gathered with two movement sensors by role playing of school violence and daily-life activities. Altogether 9 kinds of activities are recorded. Time domain features and frequency domain features are extracted and filtered by an improved Relief-F algorithm. Then the authors build a two-level classifier. The first level is a Decision Tree classifier which separates the activity of jump from the others, and the second level is a Radial Basis Function neural network which classifies the remainder 8 kinds of activities. Finally a decision layer fusion algorithm combines the recognition results of the two sensors together. The average recognition accuracy of school violence reaches 84.4%, and that of daily-life reaches 97.3%.