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Advancing Objective Mobile Device Use Measurement in Children Ages 6-11 Through Built-In Device Sensors: A

Olivia L Finnegan¹, R Glenn Weaver¹, Hongpeng Yang²

¹Department of Exercise Science, University of South Carolina, Columbia, South Carolina, USA.

Human Behavior and Emerging Technologies

|December 19, 2025

Summary

Machine learning models accurately identified individual children using iPads via sensor data, improving screen time research. This technology shows promise for objectively tracking child device usage without self-reports.

Keywords:

digital media use mobile screen use objective measurement

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Area of Science:

Digital media and child development
Human-computer interaction
Machine learning applications

Background:

Mobile device integration into children's lives is widespread.
Current screen time measurement methods (self-report, passive sensing) have limitations in accuracy and user identification.
Behavioral biometric authentication offers a potential solution for objective user identification.

Purpose of the Study:

To assess the preliminary accuracy of machine learning models in identifying unique child users of iPads using sensor data.
To explore the feasibility of using mobile device sensors for continuous user authentication in screen time research.

Main Methods:

Collected iPad sensor data (accelerometer, gyroscope, magnetometer) from nine children (ages 6-11).
Developed and trained five machine learning models (logistic regression, support vector machine, neural net, k-nearest neighbors, random forest) using 57 features.
Evaluated model performance using F1 score, accuracy, precision, and recall with an 80%-20% train-test split.

Main Results:

Machine learning models demonstrated high performance in identifying unique iPad users.
Random forest and k-nearest neighbors models achieved the highest F1 scores (0.94).
F1 scores for all models ranged from 0.75 to 0.94.

Conclusions:

Existing mobile device sensors can be effectively utilized for continuous user identification.
This approach has significant potential to enhance the accuracy of child screen time measurement.
Further research with larger samples and in real-world settings is warranted.