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

TL;DR

This paper tackles the problem of fine-grained children age-group detection from their interaction with mobile devices, focusing on a Drawing Test performed with a stylus on a tablet. It introduces a Children-Computer Interaction (CCI) pipeline that extracts 25 time-series features from the drawing process, selects the most discriminative ones, and classifies using either DBA or HMM, with HMM+SFS delivering the best performance. The study on the ChildCIdb database achieves an average accuracy of 85.39% and an average age-group distance $AGD$ of 0.17 across seven educational-age groups, significantly outperforming prior global-feature approaches. This work demonstrates the potential of time-series analysis of motor-cognitive interactions to enable age-appropriate technology environments and informs future longitudinal and privacy-aware research in e-health and e-learning contexts.

Abstract

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.

Figures (6)

• Figure 1: Architecture of the proposed approach to detect the children's age group through the interaction of the children with mobile devices and the automatic analysis of the time series generated. Blue dashed arrows represent the different configurations studied in this article in terms of Time Series and Classification. First, children perform the input stage by colouring a tree on a tablet device using a stylus as an acquisition tool (Drawing Test). Next, time series extraction and selection are performed. Finally, to predict the children's age group, two different classifiers are tested independently, DBA and HMM.
• Figure 2: Statistical Analysis + DBA: Average DTW distance calculated for each time serie considered. The green line refers to the 70th percentile of DTW distance. We highlight in dark blue the selected time series.
• Figure 3: Statistical Analysis + HMM: Results achieved using different HMM parameters. We highlight in brown the configuration with the best performance.
• Figure 4: Statistical Analysis + HMM: Average AGD achieved for each time serie considered. The green line refers to the 70th percentile of average AGD. We highlight in brown the selected time series.
• Figure 5: SFS Algorithm for DBA and HMM: Average AGD achieved for DBA and HMM during the execution of SFS in the development stage (training + validation).