  The ability to automatically recognize a person's behavioral context can
contribute to health monitoring, aging care and many other domains. Validating
context recognition in-the-wild is crucial to promote practical applications
that work in real-life settings. We collected over 300k minutes of sensor data
with context labels from 60 subjects. Unlike previous studies, our subjects
used their own personal phone, in any way that was convenient to them, and
engaged in their routine in their natural environments. Unscripted behavior and
unconstrained phone usage resulted in situations that are harder to recognize.
We demonstrate how fusion of multi-modal sensors is important for resolving
such cases. We present a baseline system, and encourage researchers to use our
public dataset to compare methods and improve context recognition in-the-wild.
