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

Juan Carlos Ruiz-Garcia, Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Jaime Herreros-Rodriguez

TL;DR

ChildCI tackles the problem of quantifying children's motor and cognitive development via natural interactions with mobile devices. The authors propose a framework and a large, public dataset (ChildCIdb_v1) containing over 100 features per test across touch and stylus modalities, and evaluate age-group detection using SVM/RF with SFFS/GA feature selection. Key findings show that all six tests together yield about 93% accuracy in distinguishing age groups (1-3, 3-6, 6-8), with Test 6 (Drawing) often performing best; results also indicate a clear relationship between chronological age and motor-cognitive interaction patterns, supporting potential applications in e-Health and e-Learning. The work demonstrates robust feature design, rigorous evaluation, and a path toward longitudinal and cross-domain research.

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.

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

TL;DR

ChildCI tackles the problem of quantifying children's motor and cognitive development via natural interactions with mobile devices. The authors propose a framework and a large, public dataset (ChildCIdb_v1) containing over 100 features per test across touch and stylus modalities, and evaluate age-group detection using SVM/RF with SFFS/GA feature selection. Key findings show that all six tests together yield about 93% accuracy in distinguishing age groups (1-3, 3-6, 6-8), with Test 6 (Drawing) often performing best; results also indicate a clear relationship between chronological age and motor-cognitive interaction patterns, supporting potential applications in e-Health and e-Learning. The work demonstrates robust feature design, rigorous evaluation, and a path toward longitudinal and cross-domain research.

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.
Paper Structure (13 sections, 4 figures, 2 tables)

This paper contains 13 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Children Interaction: Stylus vs Finger
  4. Motor and Cognitive Development
  5. ChildCI Framework
  6. Method
  7. Feature Extraction
  8. Feature Selection
  9. Classification Algorithms
  10. Experiments and results
  11. Experimental Protocol
  12. Experimental Results
  13. Conclusion and Future Work

Figures (4)

  • Figure 1: Examples of the ChildCIdb_v1 tests performed by three different children age groups: Group 1 (1 to 3 years), Group 2 (3 to 6 years), and Group 3 (6 to 8 years). From Test 1-4, red marks indicate a poor interaction of the child compared to the expected in the test. Green marks indicate correct interaction. These marks are included here for a better comprehension. Full video recordings of the different educational levels are available at https://github.com/BiDAlab/ChildCIdb_v1
  • Figure 2: Examples of children age groups formed based on the motor and cognitive features proposed in this study. Three groups can be observed in each graph: Group 1 (1 to 3 years), Group 2 (3 to 6 years), and Group 3 (6 to 8 years). Each point refers to one child of ChildCIdb_v1.
  • Figure 3: Percentage of ChildCI tests completed for the 438 children data captured in ChildCIdb_v1 by their chronological ages.
  • Figure :