# Children Age Group Detection based on Human-Computer Interaction and Time Series Analysis

arXiv: https://arxiv.org/abs/2403.04574 · HTML: https://mlsanity.com/paper/2403.04574/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2024.

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

- [Overcoming Occlusions in the Wild: A Multi-Task Age Head Approach to Age Estimation](https://mlsanity.com/paper/2506.13445/) (2025)
- [Longitudinal Digital Phenotyping for Early Cognitive-Motor Screening](https://mlsanity.com/paper/2603.25673/) (2026)

## Abstract (arXiv)

This article proposes a novel Children-Computer Interaction (CCI) approach for the task of age group detection. This approach focuses on the automatic analysis of the time series generated from the interaction of the children with mobile devices. In particular, we extract a set of 25 time series related to spatial, pressure, and kinematic information of the children interaction while colouring a tree through a pen stylus tablet, a specific test from the large-scale public ChildCIdb database. A complete analysis of the proposed approach is carried out using different time series selection techniques to choose the most discriminative ones for the age group detection task: i) a statistical analysis, and ii) an automatic algorithm called Sequential Forward Search (SFS). In addition, different classification algorithms such as Dynamic Time Warping Barycenter Averaging (DBA) and Hidden Markov Models (HMM) are studied. Accuracy results over 85% are achieved, outperforming previous approaches in the literature and in more challenging age group conditions. Finally, the approach presented in this study can benefit many children-related applications, for example, towards an age-appropriate environment with the technology.