A Multi-sensor School Violence Detecting Method Based on Improved Relief-F and D-S Algorithms
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A Multi-sensor School Violence Detecting Method Based on Improved Relief-F and D-S Algorithms Liang Ye 1,2
&
Jifu Shi 1 & Hany Ferdinando 2,3 & Tapio Seppänen 4 & Esko Alasaarela 2
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Improved Relief-F . Improved D-S . School violence . Activity recognition . Artificial intelligence
1 Introduction Activity recognition has become a popular topic in the fields of machine learning and artificial intelligence as movement sensor techniques become more and more mature. Activity recognition has a wide application prospect, e.g. smart home and smart city [1–3]. On the other hand, as scientific
* Liang Ye [email protected] 1
Department of Information and Communication Engineering, Harbin Institute of Technology, No.2 Yikuang Street, 150080 Harbin, China
2
Health and Wellness Measurement research group, OPEM unit, University of Oulu, Pentti Kaiteran katu 1, 90014 Oulu, Finland
3
Department of Electrical Engineering, Petra Christian University, Siwalankerto 121 -
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