Affordances support interaction by suggesting actions, while anti-affordances discourage or complicate task execution. In this work, we focus on property-based affordances and anti-affordances arising from a device’s physical properties, such as form, texture, and volume. We investigate how the usability of a handheld device changes when the property-based affordance of its grip transitions into an anti-affordance through a shape change. In a workshop exploring the design space for such transitions, which we term ’Useful Uselessness,’ we gathered concept ideas for shape-changing grips that can complicate or interrupt a task. Implementing three representative examples allowed us to study these grips during an exemplary drilling task. Our results show that changes in grip property can be especially useful for task interruption. We found that task context matters, and that shape-change type and its property have to be carefully chosen. In summary, we show that dynamically adapted property-based affordances can be useful to interrupt tasks, while safety issues have to be kept in mind.

On a visualization, the position of the marks encoding data is the most expressive and effective visual channel. It conveys order and quantity without impairing the perception of other visual channels. In the field of Information Visualization, position is often restricted to two dimensions, because using the third dimension, depth, usually affects the perception of size, which is also one of the most effective visual channels.

With the rapid development of microgesture research, numerous recognition devices with different sensors (e.g. force-sensitive, IMU), form factors (e.g. glove, ring) and recognition algorithms (e.g. ad-hoc, random forest) have become available. With this increasing number and diversity of recognition devices, it is important to be able to easily switch between devices and/or modify the set of recognized microgestures. To address these challenges, we propose µPoly, a first microgesture toolkit based on the µGlyph microgesture notation. µPoly takes as input the µGlyph descriptions of elementary microgestures. These inputs are combined to recognize complex microgestures described using µGlyph. µPoly therefore offers a standard for microgesture events. The design of µPoly is based on properties taken from the toolkit literature. To highlight these properties: we present (1) how practitioners are using the toolkit in four scenarios, and report on user testing and (2) how applications are being developed with µPoly.

International audience

International audience

Microgestures, i.e . fast and subtle finger movements, have shown a high potential for ubiquitous interaction. However, work to-date either focuses on grasp contexts (holding an object) or on the free-hand context (no held object). These two contexts influence the microgestures feasibility. Researchers have created sets of microgesture feasible across the entire taxonomy of everyday grasps, called transferable microgestures. However, those sets include a limited number of microgestures as compared to those for the free-hand context, for which microgestures are distinguished according to fine characteristics such as the part of the finger being touched or the number of fingers used. We provide knowledge and methods for identifying and recognizing microgestures that can transfer across contexts. First, we report a study on ergonomics factors that influence the feasibility of a microgesture in a given context. Then, we propose a conceptual model serving as a tool to determine the feasibility of a microgesture in a given context without the need for time-consuming user studies. As expected, not all microgestures were transferable to all considered contexts. Thus, we then expose two different ways of defining a set of microgestures transferable between free-hand and grasping contexts. Finally, we report a user study on recognition factors of a transferable microgesture set. • Provides the results of a study on the feasibility of transferable microgestures. • Defines a set of rules for predictively determining the feasibility of microgestures. • Explores design of transferable microgesture sets for free-hand and grasp contexts. • Empirically demonstrates that different phalanxes can be used in grasp contexts.

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.

Humans can distinguish two short simultaneous touches on the forearm, called Two-Point Discrimination (2PD), starting at a distance of approximately 40 mm. However, similar insights are lacking for vibrotactile stimuli, which would be essential for designing ergonomic gesture wristbands. Thus, an oriented 2PD test was performed using vibrotactile actuators on the forearm placed around the wrist (transverse) and along the forearm (longitudinal). The results indicate that the orientation between two actuators cannot be robustly distinguished. Two simultaneous stimuli on the forearm can best be detected at 90 mm distance, with a success rate of up to 96% for transverse placement when the actuators are arranged around the wrist. In this case, the actuators are approximately placed opposite each other, looking at the wrist as a circle. Thus, an actuator arrangement around the wrist is most sufficient to distinguish between a single and two simultaneous vibrotactile stimuli.

We use simple stickers on a wooden board to create ubiquitous touch interaction.Resonant frequencies make the board vibrate, creating tactile feedback when touching the stickers.In an experiment, we used stickers of three sizes to create Resonant Sticker Buttons and varied the delay of the feedback.Both size and feedback delay influenced the perceived weight of the buttons.While higher latencies result in a heavier perceived button, larger button sizes result in lighter perceived buttons and perceived feedback strength, and vice versa.Our findings suggest that touch interfaces with buttons of varying sizes, weights, and vibration strengths can be created on everyday surfaces, such as tables, by simply using stickers and speakers.

In the active field of hand microgestures, microgesture descriptions are typically expressed informally and are accompanied by images, leading to ambiguities and contradictions. An important step in moving the field forward is a rigorous basis for precisely describing, comparing, and analyzing microgestures. Towards this goal, we propose µGlyph, a hybrid notation based on a vocabulary of events inspired by finger biomechanics. First, we investigate the expressiveness of µGlyph by building a database of 118 microgestures extracted from the literature. Second, we experimentally explore the usability of µGlyph. Participants correctly read and wrote µGlyph descriptions 90% of the time, as compared to 46% for conventional descriptions. Third we present tools that promote µGlyph usage, including a visual editor with LaTeX export. We finally describe how µGlyph can guide research on designing, developing, and evaluating microgesture interaction. Results demonstrate the strong potential of µGlyph to establish a common ground for microgesture research.

Mid-air pointing is widely used for 3D selection in Mixed Reality but leads to arm fatigue. In a first exploratory experiment we study a two-phase design and compare modalities for each phase: mid-air gestures, eye-gaze and microgestures. Results suggest that eye-gaze and microgestures are good candidates to reduce fatigue and improve interaction speed. We therefore propose two 3D selection techniques: Look&MidAir and Look&Micro. Both techniques include a first phase during which users control a cone directed along their eye-gaze. Using the flexion of their non-dominant hand index finger, users pre-select the objects intersecting this cone. If several objects are pre-selected, a disambiguation phase is performed using direct mid-air touch for Look&MidAir or thumb to finger microgestures for Look&Micro. In a second study, we compare both techniques to the standard raycasting technique. Results show that Look&MidAir and Look&Micro perform similarly. However they are 55% faster, perceived easier to use and are less tiring than the baseline. We discuss how the two techniques could be combined for greater flexibility and for object manipulation after selection.

The representations of microgestures are essentials for researchers presenting their results through academic papers and system designers proposing tutorials to novice users. However, those representations remain disparate and inconsistent. As a first attempt to investigate how to best graphically represent microgestures, we created 21 designs, each depicting static and dynamic versions of 4 commonly used microgestures (tap, swipe, flex and hold). We first studied these designs in a quantitative online experiment with 45 participants. We then conducted a qualitative laboratory experiment in Augmented Reality with 16 participants. Based on the results, we provide design guidelines on which elements of a microgesture should be represented and how. In particular, it is recommended to represent the actuator and the trajectory of a microgesture. Also, although preferred by users, dynamic representations are not considered better than their static counterparts for depicting a microgesture and do not necessarily result in a better user recognition.