BullyDetect: Detecting School Physical Bullying With Wi-Fi and Deep Wavelet Transformer

Bo Lan, Fei Wang, Lekun Xia, Fan Nai, Shiqiang Nie, Han Ding, Jinsong Han

Abstract: More than 246 million children and adolescents suffer from school violence and bullying, e.g., verbal harassment, social harassment, and physical bullying, every year, according to a report from the United Nations Educational, Scientific and Cultural Organization. School violence and bullying severely harm the physical and emotional well-being of the victims, increasing the risks of depression, anxiety, sleep difficulties, lower academic achievement, dropping out of school, and even suicide attempts. Since school physical bullying always happens in the low-visibility areas spots of surveillance cameras, in this article, we propose to utilize Wi-Fi, a widely deployed infrastructure, to detect school physical bullying. We design residual wavelet transformer networks to conduct noise removal and action feature learning in an end-to-end manner. Besides, we propose two data augmentation methods in the temporal domain of Wi-Fi signals to simulate the different speeds and extents of bullying actions performed. Extensive evaluation of 20-paired volunteers demonstrates that 1) Wi-Fi can effectively detect physical school bullying; 2) the proposed approaches outperform long-short-time-memory networks, ResNet-1D, vision transformer, etc.; and 3) the proposed data augmentation methods can work as plug-and-play modules to improve the detection accuracy of all the above-mentioned approaches.