Activity Graph Feature Selection for Activity Pattern Classification

Abstract

Sensor-based activity recognition is attracting growing attention in many applications. Several studies have been performed to analyze activity patterns from an activity database gathered by activity recognition. Activity pattern classification is a technique that predicts class labels of people such as individual identification, nationalities, and jobs. For this classification problem, it is important to mine discriminative features reflecting the intrinsic patterns of each individual. In this paper, we propose a framework that can classify activity patterns effectively. We extensively analyze activity models from a classification viewpoint. Based on the analysis, we represent activities as activity graphs by combining every combination of daily activity sequences in meaningful periods. Frequent patterns over these activity graphs can be used as discriminative features, since they reflect people's intrinsic lifestyles. Experiments show that the proposed method achieves high classification accuracy compared with existing graph classification techniques.