Design of a bullying detection/alert system for school-wide intervention


In this paper we propose a bullying detection/alert system for school-wide intervention that combines wearables with heartrate (HR) monitors, surveillance cameras, multimodal machine learning, cloud computing, and mobile devices. The system alerts school personnel when potential bullying is detected and identi?es potential bullying in three ways: (i) by tracking and assessing the proximity of known bullies to known students at risk for bullying; (ii) by monitoring stress levels of students via HR analysis; and (iii) by rec-ognizing actions, emotions, and crowd formations associated with bullying. We describe each of these components and their integration, noting that it is possible for the system to use only a network of surveillance cameras. Alerts produced by the system can be logged. Reviews of these logs and tagged videos of detected bullying would allow school personnel to review incidents and their methods for handling bullying by providing more information about the locations, causes, and actors involved in bullying as well as teacher/staff response rates. In addition, false positives could be marked and fed back to the system for relearning and continuous improvement of the system.

Keywords School bullying, Machine learning , Heart-rate monitoring , Face tracking,  Emotion classi?cation  Action classification , Computer technology

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