We introduce TapStrapGest , a novel solution for customizable ring-based multi-finger gestures, encompassing the process from gesture elicitation to gesture recognition. Recognizing the growing demand for intuitive and customizable gesture interaction with fingers, TapStrapGest uses Tap Strap to enable users to perform simple and complex multi-finger gestures using smart rings. We conducted a gesture elicitation study, detailing the systematic process of soliciting and refining a custom set of user-defined ring-based finger gestures through participatory design and ergonomic considerations, including thinking time, goodness of fit, and memorization. Subsequently, we delve into the technical underpinnings of gesture recognition. We reduce the dimensionality of a dataset of 27 gesture classes from 21 to 15 by filtering, then from 15 to 5 by a Principal Component Analysis. We implement and compare four machine learning algorithms to show that a Quadratic Discriminant Analysis (precision=99.33%, recall=99.26%, and F1-score=99.26%) outperforms three other machine learning classifiers, i.e., a Linear Discriminant Analysis, a Support Vector Machines, and a Random Forest, as well as existing recognizers from the literature, to accurately recognize such gestures without the need to call for Deep Learning. Through a performance analysis, we demonstrate that TapStrapGest is a versatile and admissible solution for ring-based multi-finger gesture interaction, opening avenues for "eyes-free" or "screen-free" human-computer interaction in various domains.

The body of knowledge accumulated by gesture elicitation studies (GES), although useful, large, and extensive, is also heterogeneous, scattered in the scientific literature across different venues and fields of research, and difficult to generalize to other contexts of use represented by different gesture types, sensing devices, applications, and user categories. To address such aspects, we introduce RepliGES, a conceptual space that supports (1) replications of gesture elicitation studies to confirm, extend, and complete previous findings, (2) reuse of previously elicited gesture sets to enable new discoveries, and (3) extension and generalization of previous findings with new methods of analysis and for new user populations towards consolidated knowledge of user-defined gestures. Based on RepliGES, we introduce GEStory, an interactive design space and visual tool, to structure, visualize and identify user-defined gestures from a number of 216 published gesture elicitation studies.

We show how two recently introduced visual tools, RepliGES and GEStory, can be used conjointly to inform possible replications of Gesture Elicitation Studies (GES) with a case study centered on gestures that can be sensed with radars. Starting from a GES identified in GEStory, we employ the dimensions of the RepliGES space to enumerate eight possible ways to replicate that study towards gaining new insights into end user’s preferences for gesture-based interaction for applications that use radar sensors.

This paper presents theoretical and empirical results about user-defined gesture preferences for squeezable objects by focusing on a particular object: a deformable cushion. We start with a theoretical analysis of potential gestures for this squeezable object by defining a multi-dimension taxonomy of squeeze gestures composed of 82 gesture classes. We then empirically analyze the results of a gesture elicitation study resulting in a set of N=32 participants X 21 referents = 672 elicited gestures, further classified into 26 gesture classes. We also contribute to the practice of gesture elicitation studies by explaining why we started from a theoretical analysis (by systematically exploring a design space of potential squeeze gestures) to end up with an empirical analysis (by conducting a gesture elicitation study afterward): the intersection of the results from these sources confirm or disconfirm consensus gestures. Based on these findings, we extract from the taxonomy a subset of recommended gestures that give rise to design implications for gesture interaction with squeezable objects.

Gesture elicitation studies represent a popular and resourceful method in HCI to inform the design of intuitive gesture commands, reflective of end-users' behavior, for controlling all kinds of interactive devices, applications, and systems. In the last ten years, an impressive body of work has been published on this topic, disseminating useful design knowledge regarding users' preferences for finger, hand, wrist, arm, head, leg, foot, and whole-body gestures. In this paper, we deliver a systematic literature review of this large body of work by summarizing the characteristics and findings ofN=216gesture elicitation studies subsuming 5,458 participants, 3,625 referents, and 148,340 elicited gestures. We highlight the descriptive, comparative, and generative virtues of our examination to provide practitioners with an effective method to (i) understand how new gesture elicitation studies position in the literature; (ii) compare studies from different authors; and (iii) identify opportunities for new research. We make our large corpus of papers accessible online as a Zotero group library at https://www.zotero.org/groups/2132650/gesture_elicitation_studies.