Gelicit

Abstract

A gesture elicitation study, as originally defined, consists of gathering a sample of participants in a room, instructing them to produce gestures they would use for a particular set of tasks, materialized through a representation called referent, and asking them to fill in a series of tests, questionnaires, and feedback forms. Until now, this procedure is conducted manually in a single, physical, and synchronous setup. To relax the constraints imposed by this manual procedure and to support stakeholders in defining and conducting such studies in multiple contexts of use, this paper presents Gelicit, a cloud computing platform that supports gesture elicitation studies distributed in time and space structured into six stages: (1) define a study: a designer defines a set of tasks with their referents for eliciting gestures and specifies an experimental protocol by parameterizing its settings; (2) conduct a study: any participant receiving the invitation to join the study conducts the experiment anywhere, anytime, anyhow, by eliciting gestures and filling forms; (3) classify gestures: an experimenter classifies elicited gestures according to selected criteria and a vocabulary; (4) measure gestures: an experimenter computes gesture measures, like agreement, frequency, to understand their configuration; (5) discuss gestures: a designer discusses resulting gestures with the participants to reach a consensus; (6) export gestures: the consensus set of gestures resulting from the discussion is exported to be used with a gesture recognizer. The paper discusses Gelicit advantages and limitations with respect to three main contributions: as a conceptual model for gesture management, as a method for distributed gesture elicitation based on this model, and as a cloud computing platform supporting this distributed elicitation. We illustrate Gelicit through a study for eliciting 2D gestures executing Internet of Things tasks on a smartphone.