Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study
Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile devic...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Human Behavior and Emerging Technologies |
| Online Access: | http://dx.doi.org/10.1155/2024/5860114 |
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| author | Olivia L. Finnegan R. Glenn Weaver Hongpeng Yang James W. White Srihari Nelakuditi Zifei Zhong Rahul Ghosal Yan Tong Aliye B. Cepni Elizabeth L. Adams Sarah Burkart Michael W. Beets Bridget Armstrong |
| author_facet | Olivia L. Finnegan R. Glenn Weaver Hongpeng Yang James W. White Srihari Nelakuditi Zifei Zhong Rahul Ghosal Yan Tong Aliye B. Cepni Elizabeth L. Adams Sarah Burkart Michael W. Beets Bridget Armstrong |
| author_sort | Olivia L. Finnegan |
| collection | DOAJ |
| description | Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant 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 output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using F1 score, accuracy, precision, and recall. Model performance was high, with F1 scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with F1 scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments. |
| format | Article |
| id | doaj-art-cd7d05fb9ff7481ab1d94b43ed0bf2e5 |
| institution | Kabale University |
| issn | 2578-1863 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Human Behavior and Emerging Technologies |
| spelling | doaj-art-cd7d05fb9ff7481ab1d94b43ed0bf2e52025-02-03T07:23:37ZengWileyHuman Behavior and Emerging Technologies2578-18632024-01-01202410.1155/2024/5860114Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept StudyOlivia L. Finnegan0R. Glenn Weaver1Hongpeng Yang2James W. White3Srihari Nelakuditi4Zifei Zhong5Rahul Ghosal6Yan Tong7Aliye B. Cepni8Elizabeth L. Adams9Sarah Burkart10Michael W. Beets11Bridget Armstrong12Department of Exercise ScienceDepartment of Exercise ScienceDepartment of Computer Science and EngineeringDepartment of Exercise ScienceDepartment of Computer Science and EngineeringDepartment of Computer Science and EngineeringDepartment of Epidemiology and BiostatisticsDepartment of Computer Science and EngineeringDepartment of Exercise ScienceDepartment of Exercise ScienceDepartment of Exercise ScienceDepartment of Exercise ScienceDepartment of Exercise ScienceMobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant 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 output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using F1 score, accuracy, precision, and recall. Model performance was high, with F1 scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with F1 scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments.http://dx.doi.org/10.1155/2024/5860114 |
| spellingShingle | Olivia L. Finnegan R. Glenn Weaver Hongpeng Yang James W. White Srihari Nelakuditi Zifei Zhong Rahul Ghosal Yan Tong Aliye B. Cepni Elizabeth L. Adams Sarah Burkart Michael W. Beets Bridget Armstrong 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 |
| title | Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study |
| title_full | Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study |
| title_fullStr | Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study |
| title_full_unstemmed | Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study |
| title_short | Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study |
| title_sort | advancing objective mobile device use measurement in children ages 6 11 through built in device sensors a proof of concept study |
| url | http://dx.doi.org/10.1155/2024/5860114 |
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