Investigating Performance and Usage of Input Methods for Soft Keyboard Hotkeys

Touch-based devices, despite their mainstream availability, do not support a unified and efficient command selection mechanism, available on every platform and application. We advocate that hotkeys, conventionally used as a shortcut mechanism on desktop computers, could be generalized as a command selection mechanism for touch-based devices, even for keyboard-less applications. In this paper, we investigate the performance and usage of soft keyboard shortcuts or hotkeys (abbreviated SoftCuts) through two studies comparing different input methods across sitting, standing and walking conditions. Our results suggest that SoftCuts not only are appreciated by participants but also support rapid command selection with different devices and hand configurations. We also did not find evidence that walking deters their performance when using the Once input method.Comment: 17+2 pages, published at Mobile HCI 202

Evaluation eines Multitouch-basierten Menüs für Magic Lenses im Vergleich zu klassischen Menüs

Die komplexe Analysen großer Datensätze stellt in der Informationsvisualisierung eine zunehmende Herausforderung dar. Mit Hilfe von Magic Lenses wird die teilweise unübersichtliche Visualisierung dieser Datensätze lokal manipuliert und vereinfacht. Dabei spielen besonders der Umfang an Filterfunktionen und wie sie verändert werden können eine Rolle. Die Vorteile eines Multitouchdisplays im Vergleich zu herkömmlicher Maus- oder Keyboardinteraktion kommen dabei zum Einsatz. Es treten jedoch stetig Probleme bei der Adaption bisheriger Menüdesigns auf. Da Magic Lenses über Menüs parametrisiert werden sollen, stellt sich die Frage welche Menü- und Interaktionsarten besser dazu geeignet sind. In dieser Arbeit wird ein Multitouch-basiertes Kontextmenü für Magic Lenses evaluiert. Es befindet sich direkt am Linsenrand und ist kompakt jedoch neuartig. Es wird die Konkurrenzfähigkeit zu einem speziell für die Studie entworfenen und implementierten klassischen, globalen Menü getestet. Dieses ist für Nutzer vertrauter, aber distanziert sich von der Linse. Die Ergebnisse werden anschließend unter quantitativen und qualitativen Punkten zusammengefasst und ausgewertet. Es zeigt sich, dass klassische Menüs mit Touchinteraktion performanter waren. Das Multitouch-basierte Kontextmenü war allerdings bei den Probanden beliebter und wurde für die Arbeit mit Linsen bevorzugt.The complex analysis of huge data sets is an increasing challenge in information visualization. With the help of emph{Magic Lenses} the somtimes confusing visualization of those data sets is being locally manipulated and simplified. Especially the amount of filter functions and how they can be altered matter. The advantages of multitouch displays in comparison to conventional mouse and keyboard interaction are used. However, there occur continual problems with the adaption of existing menu designs. Since emph{Magic Lenses} have to be parameterized with those menus the question arises which kind of menu and interaction are more fitting. In this work we evaluate a multitouch based context menu for emph{Magic Lenses}. It is located directly at the lens and compact but new to users. We test its competitiveness to a classical global menu specifically designed and implemented for this study. Users are more familiar with it but it is distanced from the lens. Finally, the results are summarized and analyzed under quantitative and qualitative points. It turns out that classical menus with touch interaction performed best. The multitouch-based context menu though was more popular with probands and was preferred for the work with lenses

Improving Multi-Touch Interactions Using Hands as Landmarks

Efficient command selection is just as important for multi-touch devices as it is for traditional interfaces that follow the Windows-Icons-Menus-Pointers (WIMP) model, but rapid selection in touch interfaces can be difficult because these systems often lack the mechanisms that have been used for expert shortcuts in desktop systems (such as keyboards shortcuts). Although interaction techniques based on spatial memory can improve the situation by allowing fast revisitation from memory, the lack of landmarks often makes it hard to remember command locations in a large set. One potential landmark that could be used in touch interfaces, however, is people’s hands and fingers: these provide an external reference frame that is well known and always present when interacting with a touch display. To explore the use of hands as landmarks for improving command selection, we designed hand-centric techniques called HandMark menus. We implemented HandMark menus for two platforms – one version that allows bimanual operation for digital tables and another that uses single-handed serial operation for handheld tablets; in addition, we developed variants for both platforms that support different numbers of commands. We tested the new techniques against standard selection methods including tabbed menus and popup toolbars. The results of the studies show that HandMark menus perform well (in several cases significantly faster than standard methods), and that they support the development of spatial memory. Overall, this thesis demonstrates that people’s intimate knowledge of their hands can be the basis for fast interaction techniques that improve performance and usability of multi-touch systems

Assessing the effectiveness of direct gesture interaction for a safety critical maritime application

Multi-touch interaction, in particular multi-touch gesture interaction, is widely believed to give a more natural interaction style. We investigated the utility of multi-touch interaction in the safety critical domain of maritime dynamic positioning (DP) vessels. We conducted initial paper prototyping with domain experts to gain an insight into natural gestures; we then conducted observational studies aboard a DP vessel during operational duties and two rounds of formal evaluation of prototypes - the second on a motion platform ship simulator. Despite following a careful user-centred design process, the final results show that traditional touch-screen button and menu interaction was quicker and less erroneous than gestures. Furthermore, the moving environment accentuated this difference and we observed initial use problems and handedness asymmetries on some multi-touch gestures. On the positive side, our results showed that users were able to suspend gestural interaction more naturally, thus improving situational awareness

The Roly-Poly Mouse: Designing a Rolling Input Device Unifying 2D and 3D Interaction

International audienceWe present the design and evaluation of the Roly-Poly Mouse (RPM), a rolling input device that combines the advantages of the mouse (position displacement) and of 3D devices (roll and rotation) to unify 2D and 3D interaction. Our first study explores RPM gesture amplitude and stability for different upper shapes (Hemispherical, Convex) and hand postures. 8 roll directions can be performed precisely and their amplitude is larger on Hemispherical RPM. As minor rolls affect translation, we propose a roll correction algorithm to support stable 2D pointing with RPM. We propose the use of compound gestures for 3D pointing and docking, and evaluate them against a commercial 3D device, the SpaceMouse. Our studies reveal that RPM performs 31% faster than the SpaceMouse for 3D pointing and equivalently for 3D rotation. Finally, we present a proof-of-concept integrated RPM prototype along with discussion on the various technical challenges to overcome to build a final integrated version of RPM

Longer Delays in Rehearsal-based Interfaces Increase Expert Use

Rehearsal-based interfaces are designed to encourage a transition from novice to expert, but many users fail to make this transition. Most of these interfaces activate novice mode after a short delay, between 150 and 500ms. Our work investigates the impact of this delay time on expert usage and learning in three crowdsourced experiments. The first experiment examines an 8-item marking menu with delay times ranging from 200ms to 2~seconds. Results show longer delays increase successful expert selections. The second and third experiments generalise this result to a different rehearsal-based menu, a desktop clone of FastTap with 8-items and 15-items. Together, our results show that expert use correlates with delay time, but delay time does not always improve menu memorisation. However, imperceptible delays of 200ms harm long term retention of menu items. Designers of rehearsal-based interfaces should take advantage of longer delays to encourage a transition to expert usage

Réduire l'Aversion aux Erreurs pour Aider la Transition Novice-Expert avec Fast Tap

National audienceExpert interaction techniques such as gestures or hotkeys are more efficient than traditional WIMP techniques because it is often faster to recall a command than to navigate to it. However, many users seem to be reluctant to switch to expert interaction. We hypothesize the cause might be the aversion of making errors. To test this, we designed two intermediate modes for the FastTap interaction technique, allowing quick confirmation of what the user has retrieved from memory, and quick adjustment if she has made an error. We investigated the impact of these modes and of various error costs in a controlled study (N=36). We found that participants adopted the intermediate modes, that these modes reduced error rate when error cost was high, and that they did not substantially change selection times. However, while it validates the design of our intermediate modes, we found no evidence of greater switch to memory-based interaction, suggesting that reducing the error rate is not sufficient to promote expert use of techniques.Les techniques d'interaction expertes comme les vocabulaires gestuels ou les raccourcis clavier sont plus efficaces que les techniques WIMP traditionnelles. Il est en effet plus rapide de se rappeler une commande plutôt que de la retrouver dans des menus. Cependant, la plupart des utilisateurs semblent réticents à passer aux interactions qui se basent sur leur mémoire. Nous pensons que la cause pourrait être due à leur aversion à faire des erreurs. Pour tester cette hypothèse, nous avons conçu deux modes intermédiaires pour la technique d'interaction FastTap, qui permet de rapidement confirmer ce que l'utilisateur s'est rappelé de mémoire, et d'ajuster si une erreur a été faite. Nous avons étudié l'impact de ces deux modes intermédiaires et de différents coûts d'erreur dans une étude contrôlée (N=36). Nous avons trouvé que les participants ont adopté les modes intermédiaires, que ces modes réduisaient le taux d'erreur quand le coût de l'erreur était important, et qu'ils n'ont pas affecté de manière significative les temps de sélection. Cependant, bien que les résultats valident la conception de nos modes intermédiaires, nous n'avons pas trouvé de preuve sur un plus grand passage aux interactions qui se basent sur la mémoire. Cela suggère que réduire le taux d'erreur n'est pas suffisant pour promouvoir l'utilisation experte des techniques

An Exploration of Multi-touch Interaction Techniques

Research in multi-touch interaction has typically been focused on direct spatial manipulation; techniques have been created to result in the most intuitive mapping between the movement of the hand and the resultant change in the virtual object. As we attempt to design for more complex operations, the effectiveness of spatial manipulation as a metaphor becomes weak. We introduce two new platforms for multi-touch computing: a gesture recognition system, and a new interaction technique. I present Multi-Tap Sliders, a new interaction technique for operation in what we call non-spatial parametric spaces. Such spaces do not have an obvious literal spatial representation, (Eg.: exposure, brightness, contrast and saturation for image editing). The multi-tap sliders encourage the user to keep her visual focus on the tar- get, instead of requiring her to look back at the interface. My research emphasizes ergonomics, clear visual design, and fluid transition between modes of operation. Through a series of iterations, I develop a new technique for quickly selecting and adjusting multiple numerical parameters. Evaluations of multi-tap sliders show improvements over traditional sliders. To facilitate further research on multi-touch gestural interaction, I developed mGestr: a training and recognition system using hidden Markov models for designing a multi-touch gesture set. Our evaluation shows successful recognition rates of up to 95%. The recognition framework is packaged into a service for easy integration with existing applications