# Child-Computer Interaction with Mobile Devices: Recent Works, New Dataset, and Age Detection

arXiv: https://arxiv.org/abs/2102.01405 · HTML: https://mlsanity.com/paper/2102.01405/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [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)
- [Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB](https://mlsanity.com/paper/2006.00825/) (2020)
- [Characterization of the Handwriting Skills as a Biomarker for Parkinson Disease](https://mlsanity.com/paper/1903.08226/) (2019)

## Built on by (most cited first, 3 of 3)

- [GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning](https://mlsanity.com/paper/2110.03967/) (2021)
- [ChildCI Framework: Analysis of Motor and Cognitive Development in Children-Computer Interaction for Age Detection](https://mlsanity.com/paper/2204.04236/) (2022)
- [Longitudinal Digital Phenotyping for Early Cognitive-Motor Screening](https://mlsanity.com/paper/2603.25673/) (2026)

## Abstract (arXiv)

This article provides an overview of recent research in Child-Computer Interaction with mobile devices and describe our framework ChildCI intended for: i) overcoming the lack of large-scale publicly available databases in the area, ii) generating a better understanding of the cognitive and neuromotor development of children along time, contrary to most previous studies in the literature focused on a single-session acquisition, and iii) enabling new applications in e-Learning and e-Health through the acquisition of additional information such as the school grades and children's disorders, 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 is the first database in the literature that 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.