MAGIC

Gestures for interfaces should be short, pleasing, intuitive, and easily recognized by a computer.
However, it is a challenge for interface designers to create gestures easily distinguishable from
users’ normal movements. Our tool MAGIC Summoning addresses this problem. Given a specific
platform and task, we gather a large database of unlabeled sensor data captured in the environments
in which the system will be used (an “Everyday Gesture Library” or EGL). The EGL is quantized
and indexed via multi-dimensional Symbolic Aggregate approXimation (SAX) to enable quick
searching. MAGIC exploits the SAX representation of the EGL to suggest gestures with a low
likelihood of false triggering. Suggested gestures are ordered according to brevity and simplicity,
freeing the interface designer to focus on the user experience. Once a gesture is selected, MAGIC
can output synthetic examples of the gesture to train a chosen classifier (for example, with a hidden
Markov model). If the interface designer suggests his own gesture and provides several examples,
MAGIC estimates how accurately that gesture can be recognized and estimates its false positive rate
by comparing it against the natural movements in the EGL. We demonstrate MAGIC’s effectiveness
in gesture selection and helpfulness in creating accurate gesture recognizers.