Detecting Swipe Errors on Touchscreens using Grip Modulation

We show that when users make errors on mobile devices they make immediate and distinct physical responses that can be observed with standard sensors. We used three standard cognitive tasks (Flanker, Stroop and SART) to induce errors from 20 participants. Using simple low-resolution capacitive touch sensors placed around a standard mobile device and the built-in accelerometer, we demonstrate that errors can be predicted at low error rates from micro-adjustments to hand grip and movement in the period shortly after swiping the touchscreen. Specifically, when combining features derived from hand grip and movement we obtain a mean AUC of 0.96 (with false accept and reject rates both below 10%). Our results demonstrate that hand grip and movement provide strong and low latency evidence for mistakes. The ability to detect user errors in this way could be a valuable component in future interaction systems, allowing interfaces to make it easier for users to correct erroneous inputs.