  Designing effective, usable, and widely adoptable stroke gesture commands for
graphical user interfaces is a challenging task that traditionally involves
multiple iterative rounds of prototyping, implementation, and follow-up user
studies and controlled experiments for evaluation, verification, and
validation. An alternative approach is to employ theoretical models of human
performance, which can deliver practitioners with insightful information right
from the earliest stages of user interface design. However, very few aspects of
the large spectrum of human performance with stroke gesture input have been
investigated and modeled so far, leaving researchers and practitioners of
gesture-based user interface design with a very narrow range of predictable
measures of human performance, mostly focused on estimating production time, of
which extremely few cases delivered accompanying software tools to assist
modeling. We address this problem by introducing "Omnis Praedictio" (Omnis for
short), a generic technique and companion web tool that provides accurate
user-independent estimations of any numerical stroke gesture feature, including
custom features specified in code. Our experimental results on three public
datasets show that our model estimations correlate on average r > .9 with
groundtruth data. Omnis also enables researchers and practitioners to
understand human performance with stroke gestures on many levels and,
consequently, raises the bar for human performance models and estimation
techniques for stroke gesture input.
