Section: New Results

Interaction Machine

Several of our new results this year contributed to our global objective of building an Interaction Machine, especially at the micro-dynamics level. Our work on prediction algorithms and our hybrid hardware-software latency compensation method 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 the characterization of the dimensions of touch interaction, especially angle of touch, highlighted the need for additional dimensions in input events that are not yet accessible in actual systems. All in all, this confirm our hypothesis that we have to redefine input management and input events propagation in order to better account for human factors in interactive systems, to extend the possibilities for designing more efficient and expressive interaction methods.

At the meso-dynamics level, our work on improving basic interaction methods in non-standard setups (e. g., VR, AR) highlighted the need for more open and flexible system architectures and tools that ease the design and prototyping of alternative interaction techniques based on mixed modalities. The new prototyping and programing tools that we proposed this year (StEM, ZenStates and Polyphony) are our first explorations toward such system-integrated frameworks dedicated to interaction.