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AuthorAlhaddad, Ahmad Yaser
AuthorAl-Ali, Abdulaziz
AuthorPandey, Amit Kumar
AuthorCabibihan, John John
Available date2023-11-22T06:43:42Z
Publication Date2022-01-01
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Identifierhttp://dx.doi.org/10.1007/978-3-031-24670-8_8
CitationAlhaddad, A. Y., Al-Ali, A., Pandey, A. K., & Cabibihan, J. J. (2022, December). A Framework for Assistive Social Robots for Detecting Aggression in Children. In International Conference on Social Robotics (pp. 74-84). Cham: Springer Nature Switzerland.‏
ISBN9783031246692
ISSN03029743
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85148683924&origin=inward
URIhttp://hdl.handle.net/10576/49581
AbstractChildren during their early years might exhibit different forms of aggression against others. Aggression can be physical or relational. Physical aggression can take on different forms such as hitting, pushing, and kicking. The integration of technology, such as social robots, can be used to address aggression among children during childhood. In this study, we present a framework consisting of using sensory modules to detect undesirable physical interactions and social robots to provide a feedback once a behavior is detected. The framework has been demonstrated using a commercially-available social robot (i.e., Professor Einstein) with Raspberry Pi as the sensory module. Experiments with the social robot showed a promising performance of this integration. The outcomes can be used to teach children how to interact with others in an acceptable manner. The proposed framework can be integrated by social roboticists into their designs to create more dynamic interactions targeting aggressive and unwanted interactions.
Languageen
Publisherspringer link
SubjectAggression
Autism
Challenging behaviors
Safety
Social robots
TitleA Framework for Assistive Social Robots for Detecting Aggression in Children
TypeConference Paper
Pagination74-84
Volume Number13818 LNAI
dc.accessType Abstract Only


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