Towards Preventing Gaps in Health Care Systems through Smartphone Use: Analysis of ARKit for Accurate Measurement of Facial Distances in Different Angles

Abstract

There is a growing consensus in the global health community that the use of communication technologies will be an essential factor in ensuring universal health coverage of the world’s population. New technologies can only be used profitably if their accuracy is sufficient. Therefore, we explore the feasibility of using Apple’s ARKit technology to accurately measure the distance from the user’s eye to their smartphone screen. We developed an iOS application for measuring eyes-to-phone distances in various angles, using the built-in front-facing-camera and TrueDepth sensor. The actual position of the phone is precisely controlled and recorded, by fixing the head position and placing the phone in a robotic arm. Our results indicate that ARKit is capable of producing accurate measurements, with overall errors ranging between 0.88% and 9.07% from the actual distance, across various head positions. The accuracy of ARKit may be impacted by several factors such as head size, position, device model, and temperature. Our findings suggest that ARKit is a useful tool in the development of applications aimed at preventing eye damage caused by smartphone use.

An assistive photo capture system for pre- and post-operative analysis of facial reconstructive surgeries

“…Nissen et al proposed an application to estimate the eye-to-phone distance from different angles using ARKit. 9 Various experimental results confirmed that ARKit is capable of producing accurate distance measurements across various face poses. Mobile applications have also played an important role in helping health professionals taking better photos in various settings.…”

(The limits of) eye-tracking with iPads

“…Although time can be measured without an eye-tracker, the iPad can still offer a lot in the way of ensuring participant compliance. For example, in scenarios where the stimulus size (in visual degrees) must remain constant, the iPad can accurately measure the participant's distance from the device ( Nissen et al., 2023 ). Despite its modest accuracy, the iPad eye-tracking system can also estimate instances when the participant turns completely away from the screen.…”