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In real settings, natural body movements can be erroneously recognized bywhole-body input systems as explicit input actions. We call body activity notintended as input actions "background activity." We argue that understandingbackground activity is crucial to the success of always-available whole-bodyinput in the real world. To operationalize this argument, we contribute areusable study methodology and software tools to generate standardizedbackground activity datasets composed of data from multiple Kinect cameras, aVicon tracker, and two high-definition video cameras. Using our methodology, wecreate an example background activity dataset for a television-oriented livingroom setting. We use this dataset to demonstrate how it can be used to redesigna gestural interaction vocabulary to minimize conflicts with the real world.The software tools and initial living room dataset are publicly available(http://www.dgp.toronto.edu/~dustin/backgroundactivity/).

A Dataset of Naturally Occurring, Whole-Body Background Activity to Reduce Gesture Conflicts