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Non-Invasive Screen Exposure Time Assessment Using Wearable Sensor and Object Detection.

Xueshen Li, Steven Holiday, Matthew Cribbet

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary

    This study introduces a new wearable sensor and computer vision method to accurately measure screen time. This objective approach addresses limitations of current screen exposure assessments for health and behavioral studies.

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    Area of Science:

    • Digital Health
    • Human-Computer Interaction
    • Wearable Technology

    Background:

    • Increased digital device ownership has led to greater cumulative screen exposure.
    • Screen time is associated with significant physical and mental health risks for children and adults.
    • Existing screen time assessment methods lack objectivity, robustness, and are often invasive.

    Purpose of the Study:

    • To propose a novel, non-invasive method for objective screen time measurement.
    • To overcome the limitations of current subjective and invasive screen exposure assessment techniques.
    • To enable accurate and large-scale behavioral studies on screen usage.

    Main Methods:

    • Utilized a lightweight, wearable sensor to capture egocentric images.
    • Employed deep learning-based object detection to identify electronic screens.
    • Applied post-processing to filter image frames and estimate screen exposure duration.

    Main Results:

    • Successfully identified three types of electronic screens in diverse environments.
    • Demonstrated the feasibility of automatically assessing screen time exposure duration.
    • Validated the robustness and accuracy of the proposed non-invasive method.

    Conclusions:

    • The developed wearable sensor and computer vision system offers an objective and accurate solution for screen time measurement.
    • This novel method has significant potential for application in large-scale behavioral research.
    • Addresses the need for improved tools to study the impact of screen exposure on health.