Human Activity Recognition for the Identification of Bullying and Cyberbullying Using Smartphone Sensors

The smartphone is an excellent source of data; it possible to extrapolate sensor values and, through Machine Learning approaches, perform anomaly detection analysis characterized by human behavior. This work exploits Human Activity Recognition (HAR) models and techniques identify activity performed while filling out a questionnaire via application, which aims classify users as Bullying, Cyberbullying, Victims Cyberbullying. purpose the discuss new methodology that combines final label elicited from cyberbullying/bullying (Bully, Cyberbully, Bullying Victim, Cyberbullying Victim) (Human Recognition) individual fills questionnaire. paper starts with state-of-the-art HAR arrive at design model could recognize everyday life actions discriminate them resulting alleged bullying activities. Five activities were considered for recognition: Walking, Jumping, Sitting, Running Falling. best identification then applied Dataset derived “Smartphone Questionnaire Application” experiment previously described.