Omnis Pr{\ae}dictio: Estimating the Full Spectrum of Human Performance with Stroke Gestures

TL;DR

This paper introduces Omnis Praedictio, a versatile tool that accurately estimates various human performance metrics for stroke gestures, aiding UI design and evaluation.

Contribution

It presents a novel, generic modeling technique and web tool for predicting multiple human performance features in stroke gesture input, surpassing prior limited measures.

Findings

High correlation (r > .9) between model estimates and groundtruth data

Enables prediction of any numerical stroke gesture feature, including custom ones

Supports early-stage UI design with accurate performance estimations

Abstract

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.