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Datasets for recognition of aggressive interactions of children toward robotic toys

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journal contribution
submitted on 2023-09-20, 06:25 and posted on 2023-09-25, 09:56 authored by Ahmad Yaser Alhaddad, John-John Cabibihan, Andrea Bonarini

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.

Other Information

Published in: Data in Brief
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.dib.2020.106697

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2021

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Qatar University
  • College of Engineering - QU

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