TY - GEN
T1 - Speceye
T2 - 17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019
AU - Li, Zhengxiong
AU - Rathore, Aditya Singh
AU - Chen, Baicheng
AU - Song, Chen
AU - Yang, Zhuolin
AU - Xu, Wenyao
N1 - Publisher Copyright: © 2019 Association for Computing Machinery.
PY - 2019/6/12
Y1 - 2019/6/12
N2 - Digital devices have become a necessity in our daily life, with digital screens acting as a gateway to access a plethora of information present in the underlying device. However, these devices emit visible light through screens where long-term use can lead to significant screen exposure, further influencing users’ health. Conventional methods on screen exposure detection (e.g., photo logger) are usually privacy-invasive and expensive, further, require ideal light conditions, which are unattainable in real practice. Considering the light intensity and spectrum vary among different light sources, an effective screen spectrum estimation can provide vital information about screen exposure. To this end, we first investigate the characteristics of the junction between p-type and n-type semiconductor (i.e., PN junction) to sense the spectrum under various conditions. Empirically, we design and implement, SpecEye, an end-to-end, low cost, wearable, and privacy-preserving screen exposure detection system with a mobile application. For validating the performance of our system, we conduct comprehensive experiments with 54 commodity digital screens, at 43 distinct locations, with results showing a base accuracy of 99%, and an equal error rate (EER) approaching 0.80% under the controlled lab setup. Moreover, we assess the reliability, robustness, and performance variation of SpecEye under various real-world circumstances to observe a stable accuracy of 95%. Our real-world study indicates SpecEye is a promising system for screen exposure detection in everyday life.
AB - Digital devices have become a necessity in our daily life, with digital screens acting as a gateway to access a plethora of information present in the underlying device. However, these devices emit visible light through screens where long-term use can lead to significant screen exposure, further influencing users’ health. Conventional methods on screen exposure detection (e.g., photo logger) are usually privacy-invasive and expensive, further, require ideal light conditions, which are unattainable in real practice. Considering the light intensity and spectrum vary among different light sources, an effective screen spectrum estimation can provide vital information about screen exposure. To this end, we first investigate the characteristics of the junction between p-type and n-type semiconductor (i.e., PN junction) to sense the spectrum under various conditions. Empirically, we design and implement, SpecEye, an end-to-end, low cost, wearable, and privacy-preserving screen exposure detection system with a mobile application. For validating the performance of our system, we conduct comprehensive experiments with 54 commodity digital screens, at 43 distinct locations, with results showing a base accuracy of 99%, and an equal error rate (EER) approaching 0.80% under the controlled lab setup. Moreover, we assess the reliability, robustness, and performance variation of SpecEye under various real-world circumstances to observe a stable accuracy of 95%. Our real-world study indicates SpecEye is a promising system for screen exposure detection in everyday life.
KW - Health
KW - Privacy
KW - Screen Exposure
UR - https://www.scopus.com/pages/publications/85069230099
U2 - 10.1145/3307334.3326076
DO - 10.1145/3307334.3326076
M3 - Conference contribution
T3 - MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
SP - 103
EP - 116
BT - MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
PB - Association for Computing Machinery, Inc
Y2 - 17 June 2019 through 21 June 2019
ER -