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.

学校欺凌是一个常见的社会问题，学校暴力被认为是学校欺凌的最有害形式。幸运的是，随着运动传感器和模式识别技术的发展，可以通过人工智能检测学校暴力。本文提出了一种基于改进的Relief-F和Dempster-Shafe（DS）算法的学校暴力检测方法。两个运动传感器分别固定在对象的腰部和腿部，以收集加速度和陀螺仪数据。总共收集了9种活动，包括3种学校暴力和6种日常生活。小波滤波后，提取了39个时域特征和12个频域特征。为了减少计算成本，提出了一种改进的Relief-F算法，该算法根据分类的贡献和相关性选择特征。通过绘制选定特征的箱线图，作者发现，加速度y轴的频域能量可以将跳跃与其他活动区分开。因此，作者构建了一个两层分类器。第一层是将跳跃与其他活动分开的决策树，第二层是径向基函数（RBF）中性网络，该网络将其余八种活动分类。由于两个运动传感器独立工作，因此本文针对决策层融合提出了一种改进的DS算法。改进的DS算法在证据模型上设计了新的概率分布函数，并建立了新的融合规则，解决了融合碰撞的问题。根据仿真结果，与作者以前的工作相比，该方法提高了识别精度。正确识别了89.6％的学校暴力和95.1％的日常生活。准确度达到93.6％，准确度达到87.8％，分别比作者以前的工作高29.9％和2.7％。