Disoriented pen-gestures for identifying users around the tabletop without cameras and motion sensors

We present an effective corollary from our works in pen gesture technologies, originally intended for conventional digital screen environments, that gives significant influence when expanded into the domain of tabletop displays. By virtue of incorporating angular and velocity type "trace features" for pen gesture cognition into current classical linear classifiers, we are able to place a greater emphasis not on the similarity of the "visual representation" of the gestures, but rather, on determining exactly from which corners of the table the gestures were conceived. In other words, we can find out which one of the four users around the table is interfacing with the tabletop display at any particular time, and do this without the need for additional sensing paraphernalia. We illustrate this inception by discussing our treatment of "disoriented" pen gestures with our version of a 'manual' Monopoly board game for the tabletop.