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

arXiv: https://arxiv.org/abs/2204.04236 · HTML: https://mlsanity.com/paper/2204.04236/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 5 of 5)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations](https://mlsanity.com/paper/2009.06308/) (2020)
- [SVC-onGoing: Signature Verification Competition](https://mlsanity.com/paper/2108.06090/) (2021)
- [Child-Computer Interaction with Mobile Devices: Recent Works, New Dataset, and Age Detection](https://mlsanity.com/paper/2102.01405/) (2021)
- [Genetic algorithm for feature selection of EEG heterogeneous data](https://mlsanity.com/paper/2103.07117/) (2021)

## Built on by (most cited first, 2 of 2)

- [Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence](https://mlsanity.com/paper/2409.08700/) (2024)
- [Longitudinal Digital Phenotyping for Early Cognitive-Motor Screening](https://mlsanity.com/paper/2603.25673/) (2026)

## Abstract (arXiv)

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.