Kid on The Phone! Toward Automatic Detection of Children on Mobile Devices

TL;DR

This paper introduces techniques to automatically detect children on smart devices by analyzing behavioral differences from touchscreen and sensor data, achieving high accuracy with minimal interaction data.

Contribution

The work presents novel methods for identifying children on mobile devices using behavioral analysis from touchscreen and sensor data, validated on a new dataset.

Findings

Achieves 99% accuracy after 8 gestures using touch data.

Less than 0.5% error rate with minimal interaction.

Multi-sensor data improves detection speed to 3 gestures.

Abstract

Studies have shown that children can be exposed to smart devices at a very early age. This has important implications on research in children-computer interaction, children online safety and early education. Many systems have been built based on such research. In this work, we present multiple techniques to automatically detect the presence of a child on a smart device, which could be used as the first step on such systems. Our methods distinguish children from adults based on behavioral differences while operating a touch-enabled modern computing device. Behavioral differences are extracted from data recorded by the touchscreen and built-in sensors. To evaluate the effectiveness of the proposed methods, a new data set has been created from 50 children and adults who interacted with off-the-shelf applications on smart phones. Results show that it is possible to achieve 99% accuracy and less than 0.5% error rate after 8 consecutive touch gestures using only touch information or 5 seconds of sensor reading. If information is used from multiple sensors, then only after 3 gestures, similar performance could be achieved.