A Dataset of Naturally Occurring, Whole-Body Background Activity to Reduce Gesture Conflicts
This work addresses gesture recognition issues in always-available whole-body input systems, but it is incremental as it focuses on dataset creation and methodology rather than a breakthrough solution.
The paper tackled the problem of natural body movements being misrecognized as input actions in whole-body input systems by creating a dataset of background activity to reduce gesture conflicts, resulting in publicly available tools and an example dataset for a living room setting.
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/).