Fit2Ear: Generating Personalized Earplugs from Smartphone Depth Camera Images

Earphones, due to their deep integration into daily life, have been developed for unobtrusive and ubiquitous health monitoring. How-ever, these advanced algorithms greatly rely on the high quality sensing data. However, the data collected with universal earplugs could potentially generate undesirable noise, such as vibrations or even falling off. As a result, the algorithms may exhibit limited performance. In this regard, we build a dataset containing RGBD and IMU data captured by a smartphone. To provide a precise and solid ground truth, we employ additional control information from a robotic arm that holds the smartphone scanning ears along a pre-defined trajectory. With this dataset, we propose a tightly coupled information fusion algorithm for the ground truth ear modeling. Finally, we fabricate the earplugs and conduct an end-to-end evaluation of the wearability of the modeled earplugs in a user study.