From letters to words: Efficient stroke-based word completion for trackball text entry

We present a major extension to our previous work on Trackball EdgeWrite—a unistroke text entry method for trackballs—by taking it from a character-level technique to a word-level one. Our design is called stroke-based word completion, and it enables efficient word selection as part of the stroke-making process. Unlike most word completion designs, which require users to select words from a list, our technique allows users to select words by performing a fluid crossing gesture. Our theoretical model shows this word-level design to be 45.0 % faster than our prior model for character-only strokes. A study with a subject with spinal cord injury comparing Trackball EdgeWrite to the on-screen keyboard WiViK, both using word prediction and completion, shows that Trackball EdgeWrite is competitive with WiViK in speed (12.09 vs. 11.82 WPM) and accuracy (3.95 % vs