Lifelog Patterns Analyzation using Graph Embedding based on Deep Neural Network

TL;DR

This paper introduces a novel graph embedding approach using deep neural networks to analyze lifelog data from smart devices, enabling automatic extraction of daily behavior patterns with improved accuracy.

Contribution

It proposes a new graph-based method utilizing graph convolutional networks to effectively embed and analyze heterogeneous lifelog data for behavior pattern recognition.

Findings

Successfully extracted meaningful user patterns from lifelog data

Demonstrated improved performance over existing methods

Validated approach on UbiqLog dataset

Abstract

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.