Unimanual Pen+Touch Input Using Variations of Precision Grip Postures

Abstract

We introduce a new pen input space by forming postures with the same hand that also grips the pen while writing, drawing, or selecting. The postures contact the multitouch surface around the pen to enable detection without special sensors. A formative study investigates the effectiveness, accuracy, and comfort of 33 candidate postures in controlled tasks. The results indicate a useful subset of postures. Using raw capacitive sensor data captured in the study, a convolutional neural network is trained to recognize 10 postures in real time. This recognizer is used to create application demonstrations for pen-based document annotation and vector drawing. A small usability study shows the approach is feasible.