SpecEye: Towards Pervasive and Privacy-Preserving Screen Exposure Detection in Daily Life

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 (\textite.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, \textsfSpecEye, 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 \textsfSpecEye under various real-world circumstances to observe a stable accuracy of $95$%. Our real-world study indicates \textsfSpecEye is a promising system for screen exposure detection in everyday life.