Document Type

Article

Abstract

Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted howchildren engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing theaccuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a givendevice but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioralbiometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied toaddress this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor datato identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nineparticipants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLogwas downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while theparticipant interacted with the iPad. Five machine learning models, logistic regression (LR), support vector machine, neural net(NN), k-nearest neighbors (k-NN), and random forest (RF), were trained using 57 features generated from the sensor outputto perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance wasevaluated using F1 score, accuracy, precision, and recall. Model performance was high, with F1 scores ranging from 0.75 to0.94. RF and k-NN had the highest performance across metrics, with F1 scores of 0.94 for both models. This study highlightsthe potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen timemeasurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments.

Digital Object Identifier (DOI)

https://doi.org/10.1155/2024/5860114

Rights

© 2024 Olivia L. Finnegan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

APA Citation

Finnegan, O. L., Weaver, R. G., Yang, H., White, J. W., Srihari Nelakuditi, Zhong, Z., Rahul Ghosal, Tong, Y., Cepni, A. B., Adams, E. L., Burkart, S., Beets, M. W., & Armstrong, B. (2024). Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study. Human Behavior and Emerging Technologies, 2024(5860114). https://doi.org/10.1155/2024/5860114

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