Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Interaction Machine

Two of our contributions this year relate specifically to our Interaction Machine project.

Definition of Brain-Computer Interfaces

Regardless of the term used to designate them, Brain-Computer Interfaces are “Interfaces” between a user and a computer in the broad sense of the term. We provided a perspective to discuss how BCIs have been defined in the literature from the day the term was introduced by Jacques Vidal. From a Human-Computer Interaction perspective, we propose a new definition of Brain-Computer Interfaces as “any artificial system that transforms brain activity into input of a computer process” [24]. As they are interfaces, their definition should not include the finality and objective of the system they are used to interact with. To illustrate this, we compared BCIs with other widely used Human-Computer Interfaces, and draw analogies in their conception and purpose. This definition would help better encompassing for such interfaces in systems design, and more generally inform on how to better manage diverse forms of input in an Interaction Machine.

Software architecture for interactive systems

On the software engineering side, we have proposed a new Graphical User Interface (GUI) and Interaction framework based on the Entity-Component-System model (ECS) [22]. In this model, interactive elements (Entities) are characterized only by their data (Components). Behaviors are managed by continuously running processes (Systems) which select entities by the Components they possess. This model facilitates the handling of behaviors and promotes their reuse. It provides developers with a simple yet powerful composition pattern to build new interactive elements with Components. It materializes interaction devices as Entities and interaction techniques as a sequence of Systems operating on them. We have implemented these principles in the Polyphony toolkit in order to experiment the ECS model in the context of GUIs programming. It has proven to be useful and efficient for modeling standard interaction techniques, and we are now exploring its benefits for prototyping and implementing more advanced methods in a modular way. It also raises some interesting challenges about performance and scalability that we will explore further.

From the dynamics of interaction to an Interaction Machine

Several of our new results this year also informed our global objective of building an Interaction Machine. At the micro-dynamics level, as last year, our work on prediction algorithms and transfer functions highlighted the need for accessing low-level input data and to have flexible input management to be able to reliably predict current finger position and compensate for latency. Our work on new selection methods in 3D also highlighted the importance of easing the combination of input events from multiple sources and of data filtering to achieve better interaction. As it also leverages the real time aspect of the perception-action coupling for efficient interaction, it also confirms the need for efficient and low-latency input management stacks. These results give us the first leads to redefine input management and input events propagation in order to better account for human factors in interactive systems, and to extend the possibilities for designing more efficient and expressive interaction methods.

At the meso-dynamics level, our studies on the adoption of expert interaction techniques and of the impact of the context in performing interaction gestures highlighted the need for both adaptable and adaptive systems (e.g. context-based calibration of gesture recognition algorithms), which require more modular and flexible system architectures in order to enable real-time parametrization or even switching interaction techniques. These results also resonate with those at the micro-dynamics level, since they suggest strong links between users' behaviors and strategies (meso) and their low-level perception mechanisms (micro) that should be better taken into account in the design of interactive systems.

These conclusions and observations will be the basis for our investigations on the topic next year. We will in particular focus on the redefinition of the input stack and on applying the ECS model to the whole architecture of an interactive system.