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This paper compares the reliability of four mobile 3D scanning technologies and provides insight and recommendations as to which are of sufficient reliability for the customization of respiratory face mask. More specifically, we will compare the reliability of ARCore: Augmented Faces SDK by Google, the ARKit: Face Tracking SDK by Apple, the ScandyPro app using the raw information of the TrueDepth Structured Light sensor by Apple, and the 3DSizeMe app using the Structure Sensor by Occipital. ARKit and ARCore only provide a 3d model of the face, while providing no information of the head shape, we will compare the reliability of extrapolating the head shape from the face scan using Flame AI. ScandyPro and 3DSizeMe do not provide landmarks of the head, as such landmarks for measurements are found using a Deep-MVLM. A subset of anthropometric measurements as suggested by the standard ISO 16976-2:2015 part 2 will be used to assess the reliability of each method and device. We express the reliability of the process in terms of Standard Error of Measurement (SEM). Context: The COVID-19 pandemic has created situations where healthcare providers must wear offthe-shelf N95 masks for long and uninterrupted periods of time. Without applying excessive pressure to the face, it is often difficult to achieve the required airtight seal for the respiratory mask to be effective. Inflammation, pain, and discomfort caused by N95 masks are now the daily reality of numerous healthcare workers. This problem has ignited efforts around the globe to develop custom-fitted respiratory face mask based on the 3D scan of the face. Results: Reliabilities of different heads scanning technologies have been compared over various measurements. Preliminary results suggest that ARCore, ARKit and 3DSizeMe have sufficient reliability, but are lacking intermethods consistency.

Reliability of Mobile 3D Scanning Technologies for the Customization of Respiratory Face Masks