Datasets for recognition of aggressive interactions of children toward robotic toys

Ahmad Yaser Alhaddad; John-John Cabibihan; Andrea Bonarini

doi:10.1016/j.dib.2020.106697

Datasets for recognition of aggressive interactions of children toward robotic toys

Data Brief. 2020 Dec 26:34:106697. doi: 10.1016/j.dib.2020.106697. eCollection 2021 Feb.

Authors

Ahmad Yaser Alhaddad¹, John-John Cabibihan¹, Andrea Bonarini²

Affiliations

¹ Department of Mechanical and Industrial Engineering, Qatar University, Doha 2713, Qatar.
² Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano 20133, Italy.

Abstract

The data is related to unwanted interactions between a person and a small robotic toy based on acceleration sensor embedded within the robotic toy. Three toys were considered namely, a stuffed panda, a stuffed robot, and an excavator. Each toy was embedded with an accelerometer to record the interactions. Five different unwanted interactions were performed by adult participants and children. The considered interactions were hit, shake, throw, pickup, drop, and idle for the no interaction case. The collected data contains the magnitude of the resultant acceleration from the interactions. The data was processed by extracting the instances of interactions. A secondary dataset was created from the original one by creating artificial sequences. This data article contains the processed data that can be used to explore different machine learning models and techniques in classifying such interactions. Online repository contains the files: https://doi.org/10.7910/DVN/FHOO0Q.

Keywords: Acceleration; Human-robot interaction; Safety; Social robots.