TY - GEN
T1 - Non-Invasive Screen Exposure Time Assessment Using Wearable Sensor and Object Detection
AU - Li, Xueshen
AU - Holiday, Steven
AU - Cribbet, Matthew
AU - Bharadwaj, Anirudh
AU - White, Susan
AU - Sazonov, Edward
AU - Gan, Yu
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Cumulative screen exposure has been increased due to the explosion of digital technology ownership in the past decade for all people, including children who face exposure related risks such as obesity, eye problems, and disrupted sleep. Screen exposure is linked to physical and mental health risks among both children and adults. Current methods of screen exposure assessment have their limitations, mostly in the prospective of objectiveness, robustness, and invasiveness. In this paper, we propose a novel method to measure screen exposure time using a wearable sensor and computer vision technology. We use a customized, lightweight, wearable senor to capture egocentric images and use deep learning-based object detection module to identify the existence of electronic screens. The duration of screen exposure is further estimated using post-processing technology to filter consecutive frames regarding to the screen usage. Our method is non-invasive and robust, providing an objective and accurate means to screen exposure measurement. We conduct experiments on various environments to identify the existence of three types of screens and duration of screen exposure. The experimental results demonstrate the feasibility of automatically assessing screen time exposure and great potential to be applied in large scale experiments for behavioral study.
AB - Cumulative screen exposure has been increased due to the explosion of digital technology ownership in the past decade for all people, including children who face exposure related risks such as obesity, eye problems, and disrupted sleep. Screen exposure is linked to physical and mental health risks among both children and adults. Current methods of screen exposure assessment have their limitations, mostly in the prospective of objectiveness, robustness, and invasiveness. In this paper, we propose a novel method to measure screen exposure time using a wearable sensor and computer vision technology. We use a customized, lightweight, wearable senor to capture egocentric images and use deep learning-based object detection module to identify the existence of electronic screens. The duration of screen exposure is further estimated using post-processing technology to filter consecutive frames regarding to the screen usage. Our method is non-invasive and robust, providing an objective and accurate means to screen exposure measurement. We conduct experiments on various environments to identify the existence of three types of screens and duration of screen exposure. The experimental results demonstrate the feasibility of automatically assessing screen time exposure and great potential to be applied in large scale experiments for behavioral study.
KW - Computer vision
KW - Object detection
KW - Screen exposure measurement
KW - Wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85138127001&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138127001&partnerID=8YFLogxK
U2 - 10.1109/EMBC48229.2022.9871903
DO - 10.1109/EMBC48229.2022.9871903
M3 - Conference contribution
C2 - 36086530
AN - SCOPUS:85138127001
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4917
EP - 4920
BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Y2 - 11 July 2022 through 15 July 2022
ER -