.png){kind=icon}
.png){kind=icon}
Return
Evaluating heuristics for tabletop user segmentation based on simultaneous interaction
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2013.04.011.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
User differentiation
Heuristics
Tabletop
Collaboration
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W