  In real settings, natural body movements can be erroneously recognized by
whole-body input systems as explicit input actions. We call body activity not
intended as input actions "background activity." We argue that understanding
background activity is crucial to the success of always-available whole-body
input in the real world. To operationalize this argument, we contribute a
reusable study methodology and software tools to generate standardized
background activity datasets composed of data from multiple Kinect cameras, a
Vicon tracker, and two high-definition video cameras. Using our methodology, we
create an example background activity dataset for a television-oriented living
room setting. We use this dataset to demonstrate how it can be used to redesign
a 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/).
