カンファレンス (国際) ReflecTouch: Detecting Grasp Posture of Smartphone Using Corneal Reflection Images
Xiang Zhang (Keio University), Kaori Ikematsu, Kunihiro Kato (Tokyo University of Technology), Yuta Sugiura (Keio University)
CHI Conference on Human Factors in Computing Systems (CHI2022)
By sensing how a user is holding a smartphone, adaptive user interfaces are possible such as those that automatically switch the displayed content and position of graphical user interface (GUI) components following how the phone is being held. We propose ReflecTouch, a novel method for detecting how a smartphone is being held by capturing images of the smartphone screen reflected on the cornea with a built-in front camera. In these images, the areas where the user places their fingers on the screen appear as shadows, which makes it possible to estimate the grasp posture. Since most smartphones have a front camera, this method can be used regardless of the device model; in addition, no additional sensor or hardware is required. We conducted data collection experiments to verify the classification accuracy of the proposed method for six different grasp postures, and the accuracy was 85%.