ChildCI Framework: Analysis of Motor and Cognitive Development in Children-Computer Interaction for Age Detection
This work provides a framework for understanding children's development, with potential applications in e-Health and e-Learning, but it is incremental as it builds on existing literature and adapts features.
The paper tackled the problem of detecting children's age groups by analyzing their motor and cognitive interactions with mobile devices, achieving over 93% accuracy on a database of over 400 children aged 18 months to 8 years.
This article presents a comprehensive analysis of the different tests proposed in the recent ChildCI framework, proving its potential for generating a better understanding of children's neuromotor and cognitive development along time, as well as their possible application in other research areas such as e-Health and e-Learning. In particular, we propose a set of over 100 global features related to motor and cognitive aspects of the children interaction with mobile devices, some of them collected and adapted from the literature. Furthermore, we analyse the robustness and discriminative power of the proposed feature set including experimental results for the task of children age group detection based on their motor and cognitive behaviours. Two different scenarios are considered in this study: i) single-test scenario, and ii) multiple-test scenario. Results over 93% accuracy are achieved using the publicly available ChildCIdb_v1 database (over 400 children from 18 months to 8 years old), proving the high correlation of children's age with the way they interact with mobile devices.