Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - PreventDark
T2 - 13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
AU - Ruan, Wenjie
AU - Sheng, Quan Z.
AU - Yao, Lina
AU - Tran, Nguyen Khoi
AU - Yang, Yu Chieh
PY - 2016/3/14
Y1 - 2016/3/14
N2 - Smartphone adoption has increased significantly and users can access the Internet, communicate, and entertain themselves anywhere and anytime. However, the negative aspects of smartphone overuse on young adults are being increasingly recognized recently. One such serious problematic usage is peering at brightly lit screens in dark, which can cause sleep loss and resultant health problems. In this paper, we investigate the potential of exploiting sensors embedded in smartphones to detect and prevent such unhealthy habit by measuring the ambient light intensity and detecting the smartphone motion. We implement our system through an Android APP, called PreventDark. We show the feasibility and accuracy of our developed system by experiments on different android smartphones. Field experimental results indicate our system can significantly prevent and decrease the problematic use after intervention with up to 93.6%, particularly in the dark residential environments.
AB - Smartphone adoption has increased significantly and users can access the Internet, communicate, and entertain themselves anywhere and anytime. However, the negative aspects of smartphone overuse on young adults are being increasingly recognized recently. One such serious problematic usage is peering at brightly lit screens in dark, which can cause sleep loss and resultant health problems. In this paper, we investigate the potential of exploiting sensors embedded in smartphones to detect and prevent such unhealthy habit by measuring the ambient light intensity and detecting the smartphone motion. We implement our system through an Android APP, called PreventDark. We show the feasibility and accuracy of our developed system by experiments on different android smartphones. Field experimental results indicate our system can significantly prevent and decrease the problematic use after intervention with up to 93.6%, particularly in the dark residential environments.
U2 - 10.1109/PERCOMW.2016.7457071
DO - 10.1109/PERCOMW.2016.7457071
M3 - Conference contribution/Paper
AN - SCOPUS:84966661747
SP - 1
EP - 3
BT - 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
PB - IEEE
Y2 - 14 March 2016 through 18 March 2016
ER -