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

Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches