unistroke

EdgeWrite is a new unistroke text entry method for handheld devices designed to provide high accuracy and stability of motion for people with motor impairments. It is also effective for able-bodied people. An EdgeWrite user enters text by traversing the edges and diagonals of a square hole imposed over the usual text input area. Gesture recognition is accomplished not through pattern recognition but through the sequence of corners that are hit. This means that the full stroke path is unimportant and recognition is highly deterministic, enabling better accuracy than other gestural alphabets such as Graffiti. A study of able-bodied users showed subjects with no prior experience were 18% more accurate during text entry with Edge Write than with Graffiti (p>.05), with no significant difference in speed. A study of 4 subjects with motor impairments revealed that some of them were unable to do Graffiti, but all of them could do Edge Write. Those who could do both methods had dramatically better accuracy with Edge Write.

We present the design and implementation of a word-level stroking system called Fisch, which is intended to improve the speed of character-level unistrokes. Importantly, Fisch does not alter the way in which character-level unistrokes are made, but allows users to gradually ramp up to word-level unistrokes by extending their letters in minimal ways. Fisch relies on in-stroke word completion, a flexible design for fluidly turning unistroke letters into whole words. Fisch can be memorized at the motor level since word completions always appear at the same positions relative to the strokes being made. Our design for Fisch is suitable for use with any unistroke alphabet. We have implemented Fisch for multiple versions of EdgeWrite, and results show that Fisch reduces the number of strokes during entry by 43.9% while increasing the rate of entry. An informal test of "record speed" with the stylus version resulted in 50-60 wpm with no uncorrected errors.

Although mobile, tablet, large display, and tabletop computers increasingly present opportunities for using pen, finger, and wand gestures in user interfaces, implementing gesture recognition largely has been the privilege of pattern matching experts, not user interface prototypers. Although some user interface libraries and toolkits offer gesture recognizers, such infrastructure is often unavailable in design-oriented environments like Flash, scripting environments like JavaScript, or brand new off-desktop prototyping environments. To enable novice programmers to incorporate gestures into their UI prototypes, we present a "$1 recognizer" that is easy, cheap, and usable almost anywhere in about 100 lines of code. In a study comparing our $1 recognizer, Dynamic Time Warping, and the Rubine classifier on user-supplied gestures, we found that $1 obtains over 97% accuracy with only 1 loaded template and 99% accuracy with 3+ loaded templates. These results were nearly identical to DTW and superior to Rubine. In addition, we found that medium-speed gestures, in which users balanced speed and accuracy, were recognized better than slow or fast gestures for all three recognizers. We also discuss the effect that the number of templates or training examples has on recognition, the score falloff along recognizers' N-best lists, and results for individual gestures. We include detailed pseudocode of the $1 recognizer to aid development, inspection, extension, and testing.