ChildCI Framework: Analysis of Motor and Cognitive Development in Children-Computer Interaction for Age Detection

TL;DR

This paper evaluates the ChildCI framework's ability to analyze children's motor and cognitive development through interaction data, achieving over 93% accuracy in age detection from mobile device interactions.

Contribution

It introduces a comprehensive set of features for analyzing children's interaction patterns and demonstrates their effectiveness in age group detection.

Findings

Over 93% accuracy in age detection

Robustness of feature set across scenarios

High correlation between interaction patterns and age

Abstract

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.