  Events are fundamental for understanding how people experience their lives.
It is challenging, however, to automatically record all events in daily life.
An understanding of multimedia signals allows recognizing events of daily
living and getting their attributes as automatically as possible. In this
paper, we consider the problem of recognizing a daily event by employing the
commonly used multimedia data obtained from a smartphone and wearable device.
We develop an unobtrusive approach to obtain latent semantic information from
the data, and therefore an approach for daily event recognition based on
semantic context enrichment. We represent the enrichment process through an
event knowledge graph that semantically enriches a daily event from a low-level
daily activity. To show a concrete example of this enrichment, we perform an
experiment with eating activity, which may be one of the most complex events,
by using 14 months of data for three users. In this process, to unobtrusively
complement the lack of semantic information, we suggest a new food
recognition/classification method that focuses only on a physical response to
food consumption. Experimental results indicate that our approach is able to
show automatic abstraction of life experience. These daily events can then be
used to create a personal model that can capture how a person reacts to
different stimuli under specific conditions.
