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.

翻译：本文提出了一种新颖的儿童-计算机交互（CCI）方法用于年龄段检测任务。该方法专注于自动分析儿童与移动设备交互过程中产生的时间序列。具体而言，我们从大规模公开的ChildCIdb数据库中提取了25组与儿童在触控笔平板电脑上涂色测试相关的空间、压力及运动学信息的时间序列。通过两种时间序列选择技术（统计分析法和名为顺序前向搜索（SFS）的自动算法）对最具判别性的序列进行筛选，并研究了动态时间规整重心平均（DBA）与隐马尔可夫模型（HMM）等不同分类算法。实验结果表明，该方法在更具挑战性的年龄段分类条件下准确率超过85%，超越了现有文献方法。最后，本研究提出的方法可应用于构建适龄技术环境的儿童相关应用中。