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 evolving landscape of distributed user interfaces requires the prototyping stage also be distributed between users, tasks, platforms, and environments. To create a cohesive distribution of the user interface elements in such ecosystems, paired sketching has emerged as a collaborative design method that leverages multiple stakeholders’ strengths, including designers, developers, and end users, working in pairs. In the context of developer experience applied to paired sketching for distributed user interfaces, we decomposed a workflow into four disciplines according to the Software and Systems Process Engineering Meta-Model (SPEM) notation. First, we defined a protocol to deploy paired sketching of distributed user interfaces, supported by UbiSketch , a collaborative software environment tailored featuring sketch recognition and whiteboarding. Second, to evaluate paired sketching for engineering interactive systems, we conducted an experiment involving five pairs of stakeholders who sketched a distributed user interface for inside-the-vehicule interaction distributed on four platforms: smartphone, tablet, pen display, and tabletop. Empirical results from questionnaires, reactivity, intention, perceived satisfaction, and free comments, suggest a preference order in which the tabletop is ranked first, followed by the tablet, smartphone, and pen display. Based on these results, we discuss the potential of paired sketching for distributed user interfaces.

Despite the tremendous progress made for recognizing gestures acquired by various devices, such as the Leap Motion Controller, developing a gestural user interface based on such devices still induces a significant programming and software engineering effort before obtaining a running interactive application. To facilitate this development, we present QuantumLeap, a framework for engineering gestural user interfaces based on the Leap Motion Controller. Its pipeline software architecture can be parameterized to define a workflow among modules for acquiring gestures from the Leap Motion Controller, for segmenting them, recognizing them, and managing their mapping to functions of the application. To demonstrate its practical usage, we implement two gesture-based applications: an image viewer that allows healthcare workers to browse DICOM medical images of their patients without any hygiene issues commonly associated with touch user interfaces and a large-scale application for managing multimedia contents on wall screens. To evaluate the usability of QuantumLeap, seven participants took part in an experiment in which they used QuantumLeap to add a gestural interface to an existing application.

While end users can acquire full 3D gestures with many input devices, they often capture only 3D trajectories, which are 3D uni-path, uni-stroke single-point gestures performed in thin air. Such trajectories with their $(x,y,z)$ coordinates could be interpreted as three 2D stroke gestures projected on three planes,\ie, $XY$, $YZ$, and $ZX$, thus making them admissible for established 2D stroke gesture recognizers. To investigate whether 3D trajectories could be effectively and efficiently recognized, four 2D stroke gesture recognizers, \ie, \$P, \$P+, \$Q, and Rubine, are extended to the third dimension: $\$P^3$, $\$P+^3$, $\$Q^3$, and Rubine-Sheng, an extension of Rubine for 3D with more features. Two new variations are also introduced: $\F for flexible cloud matching and FreeHandUni for uni-path recognition. Rubine3D, another extension of Rubine for 3D which projects the 3D gesture on three orthogonal planes, is also included. These seven recognizers are compared against three challenging datasets containing 3D trajectories, \ie, SHREC2019 and 3DTCGS, in a user-independent scenario, and 3DMadLabSD with its four domains, in both user-dependent and user-independent scenarios, with varying number of templates and sampling. Individual recognition rates and execution times per dataset and aggregated ones on all datasets show a highly significant difference of $\$P+^3$ over its competitors. The potential effects of the dataset, the number of templates, and the sampling are also studied.