Omnis Prædictio: Estimating the full spectrum of human performance with stroke gestures. Issue 142 (October 2020)

• Record Type: Journal Article
• Authors: Leiva, Luis A.; Vatavu, Radu-Daniel; Martín-Albo, Daniel; Plamondon, Réjean
• Is Part Of: International journal of human-computer studies. Issue 142(2020)
• Publication Date: 2020-10
• Subjects: Stroke gestures -- Touch gestures -- Gesture user interfaces -- Human performance -- Estimation -- Prediction -- Gesture synthesis -- Kinematic theory; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2020.102466
• Languages: English
• ISSNs: 1071-5819

Abstract

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 Prædictio" (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 s > 0.9 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.