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  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

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Section: New Results


Participants : Marc Baloup, Géry Casiez, Stéphane Huot, Edward Lank, Sylvain Malacria, Mathieu Nancel, Thomas Pietrzak [correspondent] , Thibault Raffaillac, Marcelo Wanderley.

Improving interaction bandwidth and expressiveness

Despite the ubiquity of touch-based input and the availability of increasingly computationally powerful touchscreen devices, there has been comparatively little work on enhancing basic canonical gestures such as swipe-to-pan and pinch-to-zoom. We introduced transient pan and zoom, i. e., pan and zoom manipulation gestures that temporarily alter the view and can be rapidly undone [16]. Leveraging typical touchscreen support for additional contact points, we designed our transient gestures so that they co-exist with traditional pan and zoom interaction. In addition to reducing repetition in multi-level navigation, our transient pan-and-zoom also facilitates rapid movement between document states.

Image editing software feature various pixel selection tools based on geometrical (rectangle, ellipses, polygons) or semantical (magic wand, selection brushes) data from the image. They are efficient in many situations, but are limited when selecting bitmap representations of handwritten text for e. g., interpreting scanned historical documents that cannot be reliably analyzed by automatic OCR methods: strokes are thin, with many overlaps and brightness variations. We have designed a new selection tool dedicated to this purpose [27]: a cursor based brush selection tool with two additional degrees of freedom: brush size and brightness threshold. The brush cursor displays feedforward clues that indicates the user which pixels will be selected upon pressing the mouse button. This brush provides a fine grain control to the user over the selection.

Figure 3. A four-dimensional selection brush for digitized handwritten documents. Red pixels will be selected, blue pixels will not.

Interacting with specific setups (Large-Displays, Virtual & Augmented Reality)

Large displays are becoming commonplace at work, at home, or in public areas. Handheld devices such as smartphones and smartwatches are ubiquitous, but little is known on regarding how these devices could be used to point at remote large displays. We conducted a survey on possession and use of smart devices, as well as a controlled experiment comparing seven distal pointing techniques on phone or watch, one- and two-handed, and using different input channels and mappings [26]. Our results favor using a smartphone as a trackpad, but also explore performance tradeoffs that can inform the choice and design of distal pointing techniques for different contexts of use.

In virtual reality environments, raycasting is the most common target pointing technique. However, performance on small and distant targets is impacted by the accuracy of the pointing device and the user’s motor skills. Existing pointing facilitation techniques are currently only applied in the context of a virtual hand, i. e., for targets within reach. We studied how a user-controlled cursor could be added on the ray in order to enable target proximity-based pointing techniques –such as the Bubble Cursor– to be used for targets that are out of reach [17]. We conducted a study comparing several visual feedbacks for this technique (see Figure 4). Our results showed that simply highlighting the nearest target reduces the selection time by 14.8% and the error rate by 82.6% compared to standard Raycasting. For small targets, the selection time is reduced by 25.7% and the error rate by 90.8%.

Figure 4. Visual feedback for RayCursor: (a1,a2) classical Raycasting; (b) Rope Cursor: a stroke between the closest target and the cursor; (c) 3D Bubble: a bubble centered on the cursor which contains the nearest target; (d) Highlighting on the nearest target; (e,f), highlight + rope and 3D Bubble.

Brain-Computer Interfaces (BCIs) enable users to interact with computers without any dedicated movement, bringing new hands-free interaction paradigms that could be beneficial in an Augmented Reality (AR) setup. We first tested the feasibility of using BCI in AR settings based on Optical See-Through Head-Mounted Displays (OST-HMDs) [12]. Experimental results showed that a BCI and an OST-HMD equipment (EEG headset and Hololens in our case) are well compatible and that small movements of the head can be tolerated when using the BCI. Then, we introduced a design space for command display strategies based on BCI in AR, when exploiting a famous brain pattern called Steady-State Visually Evoked Potential (SSVEP). Our design space relies on five dimensions concerning the visual layout of the BCI menu: orientation, frame-of-reference, anchorage, size and explicitness. We implemented various BCI-based display strategies and tested them within the context of mobile robot control in AR. Our findings were finally integrated within an operational prototype based on a real mobile robot that is controlled in AR using a BCI and a HoloLens headset. Taken together, our results (4 user studies) and our methodology could pave the way to future interaction schemes in Augmented Reality exploiting 3D User Interfaces based on brain activity and BCIs.

More generally, we also contributed to a reflexion on the complexity and scientific challenges associated to virtual and augmented realities [29] and the challenges to make virtual environments more closely related to the real world [30].

Tools for prototyping and programming interaction

Touch interactions are now ubiquitous, but few tools are available to help designers quickly prototype touch interfaces and predict their performance. On one hand, for rapid prototyping, most applications only support visual design. On the other hand, for predictive modeling, tools such as CogTool generate performance predictions but do not represent touch actions natively and do not allow exploration of different usage contexts. To combine the benefits of rapid visual design tools with underlying predictive models, we developed the Storyboard Empirical Modeling (StEM) tool [20], [19] for exploring and predicting user performance with touch interfaces (see Figure 5). StEM provides performance models for mainstream touch actions, based on a large corpus of realistic data. We evaluated StEM in an experiment and compared its predictions to empirical times for several scenarios. The study showed that our predictions are accurate (within 7% of empirical values on average), and that StEM correctly predicted differences between alternative designs. Our tool provides new capabilities for exploring and predicting touch performance, even in the early stages of design.

Figure 5. Storyboard Empirical Modeling (StEM): (a) users drag and drop actions onto a timeline to construct an interaction sequence; (b) users can visualize prediction times for a scenario composed of different screens; (c) users can compare scenarios, and filter the predictions according to contextual factors such as screen size or user's expertise.

Following our main objective of revisiting interactive system, we have also proposed two systems for defining and programming interactive behaviors and interactions.

Much progress has been made on interactive behavior development tools for expert programmers. However, less effort has been made in investigating how these tools support creative communities who typically struggle with technical development. This is the case, for instance, of media artists and composers working with interactive environments. To address this problem, we have introduces ZenStates [18], a new specification model for creative interactive environments that combines Hierarchical Finite-States Machines, expressions, off-the-shelf components called Tasks, and a global communication system called the Blackboard. We have implemented our model in a direct manipulation-based software interface and probed ZenStates' expressive power through 90 exploratory scenarios. we have also conducted a user study to investigate the understandability of ZenStates' model. Results support ZenStates viability, its expressiveness, and suggest that ZenStates is easier to understand –in terms of decision time and decision accuracy– compared to popular alternatives such as standard object-oriented programming and a data-flow visual language.

In a more general context, we have introduced a new GUI framework based on the Entity-Component-System model (ECS), where interactive elements (Entities) can acquire any data (Components) [24]. Behaviors are managed by continuously running processes (Systems) which select entities by the components they possess. This model facilitates the handling and reuse of behaviors. It allows to define the interaction modalities of an application globally, by formulating them as a set of Systems. We have implemented an experimental toolkit based on this approach, Polyphony, in order to demonstrate the use and benefits of this model.