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 onscreen 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. 2.21% total errors), but less visually tedious and ultimately preferred. The results also show that word-level Trackball EdgeWrite is 46.5% faster and 36.7% more accurate than our subject’s prior peak performance with character-level Trackball EdgeWrite, and 75.2% faster and 40.2% more accurate than his prior peak performance with his preferred on-screen keyboard. An additional evaluation of the same subject over a two-month field deployment shows a 43.9% reduction in unistrokes due to strokebased word completion in Trackball EdgeWrite