Pre-Interaction Identification by Dynamic Grip Classification

We present a novel authentication method to identify users as they pick up a mobile device. We use a combination of back-of-device capacitive sensing and accelerometer measurements to perform classification, and obtain increased performance compared to previous accelerometer-only approaches. Our initial results suggest that users can be reliably identified during the pick-up movement before interaction commences

28 frames later: predicting screen touches from back-of-device grip changes

We demonstrate that front-of-screen targeting on mobile phones can be predicted from back-of-device grip manipulations. Using simple, low-resolution capacitive touch sensors placed around a standard phone, we outline a machine learning approach to modelling the grip modulation and inferring front-of-screen touch targets. We experimentally demonstrate that grip is a remarkably good predictor of touch, and we can predict touch position 200ms before contact with an accuracy of 18mm