Evaluating heuristics for tabletop user segmentation based on simultaneous interaction

• Differentiating users can enhance context-awareness adaptation on tabletops.
• Our algorithm for user segmentation is based on simultaneous manipulations.
• Our approach does not rely on additional hardware besides the tabletop.
• Three heuristic versions of the algorithm have been compared.
• The heuristic involving more knowledge on control distances outperforms the rest.

Differentiating between users that interact on a tabletop could be beneficial for collaborative tasks to support territoriality-oriented features such as a more efficient space management or a better presentation of the contents. In this paper, we design a novel algorithm for the user differentiation or segmentation based on the simultaneous manipulation of the controls. This is a potential differentiating factor that has remained unexplored so far, and in combination with other factors may become relevant to successfully accomplish such differentiation task. Basically it relies on the idea that users manipulate digital elements with a single hand, and hence, if two controls are being used at the same time, they most likely belong to different users. On the generic algorithm, three different versions have been implemented that include several heuristics to address the problem. The comparison under a simulated experiment shows that the heuristic involving more knowledge on distances on user controls performed better according to different goodness functions. This shows promising to further development and refinement of the approach by expanding it with other potential factors to eventually build a robust user differentiation subsystem.