Modeling human performance of pen stroke gestures

Abstract

This paper presents a quantitative human performance model of making single-stroke pen gestures within certain error constraints in terms of production time. Computed from the properties of Curves, Line segments, and Corners (CLC) in a gesture stroke, the model may serve as a foundation for the design and evaluation of existing and future gesture-based user interfaces at the basic motor control efficiency level, similar to the role of previous "laws of action" played to pointing, crossing or steeringbased user interfaces. We report and discuss our experimental results on establishing and validating the CLC model, together with other basic empirical findings in stroke gesture production.

Estimating Production Time of Touchless Hand Drawing Gestures

“…Although the definition of "needed straight-line segments" is ambiguous [13,17] (a procedure describing the reduction of gestures with curves into straight lines is missing), Isokoski's model seems conceptually easy to extend to hand gestures because it requires estimating the constant time to produce a straight line gesture segment from experimental data.…”

“…Cao and Zhai [13] suggested a model to estimate the production time of single penstroke gestures. The model considers three features found in pen-stroke gestures: Curves, Line segments, and Corners (the reason why the model is referred to CLC).…”

“…(Formula 4 can be used instead of formula 3.) The Corner, which is an abrupt change in stroke direction, was discarded by Cao and Zhai after empirical studies showed its insignificant impact on production times [13].…”

“…Considering this landscape, the generic goal of the research reported in this paper is to extend the existing models to encompass hand gestures. More specifically, our goal is to extend the CLC (Curves, Line segments and Corners) [13], Isokoski's [14] and KLM [5] models to estimate the production times of touchless hand gestures. Our research is restricted to gestures performed by young adults in normal health conditions, with basic or no experience with touchless interactions, and using the dominant hand (fingers are not considered).…”

SimpleFlow: Enhancing Gestural Interaction with Gesture Prediction, Abbreviation and Autocompletion

“…Models of human performance when following trajectories have also been investiga ted [1], and could prove useful for modeling gesture input [10,20], as models and metrics of human performance have proven useful for predictive text in put [27,29]. Related to modeling gestures is research around measuring and modeling the complexity of gestures [26], which could be applied to quantifying the predictability of gestures.…”