Authors:
Vincenzo Gattulli
and
Lucia Sarcinella
Affiliation:
Department of Computer Science, University of Bari Aldo Moro, 70125 Bari, Italy
Keyword(s):
Human Activity Recognition, Cyberbullying, Bully, Smartphone, Sensors, Machine Learning.
Abstract:
The smartphone is an excellent source of data. Sensor values can be extrapolated from the smartphone. This work exploits Human Activity Recognition (HAR) models and techniques to identify human activity performed while filling out a questionnaire that aims to classify users as Bullies, Cyberbullies, Victims of Bullying, and Victims of Cyberbullying. The paper aims to identify activities related to the questionnaire class other than just sitting. The paper starts with a state-of-the-art analysis of HAR to arrive at the design of a model that could recognize everyday life actions and discriminate them from actions resulting from alleged bullying activities (Questionnaire Personality Index). Five activities were considered for recognition: Walking, Jumping, Sitting, Running, and Falling. The best HAR activity identification model was applied to the dataset obtained from the "Smartphone Questionnaire Application" experiment to perform the analysis. The best model for HAR identification i
s CNN.
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