Child-Computer Interaction: Recent Works, New Dataset, and Age Detection

• URL: http://arxiv.org/abs/2102.01405v1
• Date: Tue, 2 Feb 2021 09:51:58 GMT
• Title: Child-Computer Interaction: Recent Works, New Dataset, and Age Detection
• Authors: Ruben Tolosana, Juan Carlos Ruiz-Garcia, Ruben Vera-Rodriguez, Jaime Herreros-Rodriguez, Sergio Romero-Tapiador, Aythami Morales, Julian Fierrez
• Abstract summary: ChildCI aims to generate a better understanding of the cognitive and neuromotor development of children while interacting with mobile devices. In our framework children interact with a tablet device, using both a pen stylus and the finger, performing different tasks that require different levels of neuromotor and cognitive skills. ChildCIdb comprises more than 400 children from 18 months to 8 years old, considering therefore the first three development stages of the Piaget's theory.
• Score: 6.061943386819384
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We overview recent research in Child-Computer Interaction and describe our framework ChildCI intended for: i) generating a better understanding of the cognitive and neuromotor development of children while interacting with mobile devices, and ii) enabling new applications in e-learning and e-health, among others. Our framework includes a new mobile application, specific data acquisition protocols, and a first release of the ChildCI dataset (ChildCIdb v1), which is planned to be extended yearly to enable longitudinal studies. In our framework children interact with a tablet device, using both a pen stylus and the finger, performing different tasks that require different levels of neuromotor and cognitive skills. ChildCIdb comprises more than 400 children from 18 months to 8 years old, considering therefore the first three development stages of the Piaget's theory. In addition, and as a demonstration of the potential of the ChildCI framework, we include experimental results for one of the many applications enabled by ChildCIdb: children age detection based on device interaction. Different machine learning approaches are evaluated, proposing a new set of 34 global features to automatically detect age groups, achieving accuracy results over 90% and interesting findings in terms of the type of features more useful for this task.

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