  Recently, as the spread of smart devices increases, the amount of data
collected through sensors is increasing. A lifelog is a kind of big data to
analyze behavior patterns in the daily life of individuals collected from
various smart de-vices. However, sensor data is a low-level signal that makes
it difficult for hu-mans to recognize the situation directly and cannot express
relations clearly. It is also difficult to identify the daily behavior pattern
because it records heterogene-ous data by various sensors. In this paper, we
propose a method to define a graph structure with node and edge and to extract
the daily behavior pattern from the generated lifelog graph. We use the graph
convolution method to embeds the lifelog graph and maps it to low dimension.
The graph convolution layer im-proves the expressive power of the daily
behavior pattern by implanting the life-log graph in the non-Euclidean space
and learns the patterns of graphs. Experi-mental results show that the proposed
method automatically extracts meaningful user patterns from UbiqLog dataset. In
addition, we confirm the usefulness by comparing our method with existing
methods to evaluate performance.
