  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.
