Physical Violence Detection for Preventing School Bullying

School bullying is a serious problem among teenagers, causing depression, dropping out of school, or even suicide. It is thus important to develop antibullying methods. This paper proposes a physical bullying detection method based on activity recognition. The architecture of the physical violence detection system is described, and a Fuzzy Multithreshold classifier is developed to detect physical bullying behaviour, including pushing, hitting, and shaking. Importantly, the application has the capability of distinguishing these types of behaviour from such everyday activities as running, walking, falling, or doing push-ups. To accomplish this, the method uses acceleration and gyro signals. Experimental data were gathered by role playing school bullying scenarios and by doing daily-life activities. The simulations achieved an average classification accuracy of 92%, which is a promising result for smartphone-based detection of physical bullying. 1. Introduction School bullying is a common social problem among teenagers. It affects the victims both mentally and physically and is considered as one of the main reasons for depression, dropping out of school, and adolescent suicide [1, 2]. In view of this, antibullying is an important and timeless topic; one that has been studied ever since the 1960s. New approaches for preventing school bullying become available as sensor technology develops. School bullying can take various forms, such as cursing and physical violence. According to a survey conducted by the authors in Finland and Indonesia, physical violence is considered as the most harmful to teenagers. Consequently, this paper will focus on detecting physical bullying. Following the popularity of smartphones, several antibullying applications have entered the market, including Stop Bullies, ICE BlackBox, Campus Safety, and Back Off Bully. They all work in roughly the following fashion. When a bullying event occurs, the victim or a witness needs to take out a smartphone, run the app, and press a button to send an alarm message. To photograph the event, they must hold the camera toward the bullies. However, this is not convenient for the victim, especially when bullied physically. Moreover, this action could infuriate the bullies and lead to harder bullying. An application that could autonomously detect a bullying event, without letting the bullies know, would be highly desirable, especially if it came with the capability to send alarm messages automatically. This paper proposes an algorithm for detecting physical bullying. It could be implemented on a