Lifelog patterns analyzation using graph embedding based on deep neural network

Abstract

Copyright © 2019, arXiv, All rights reserved. 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 devices. However, sensor data is a low-level signal that makes it difficult for humans to recognize the situation directly and cannot express relations clearly. It is also difficult to identify the daily behavior pattern because it records heterogeneous 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 improves the expressive power of the daily behavior pattern by implanting the lifelog graph in the non-Euclidean space and learns the patterns of graphs. Experimental 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.