Personalized Human Activity Recognition using Hypergraph Learning with Fusion Features

Human activity recognition (HAR) is a promising field which has a wide range of applications in medicine, electronic forensics and Internet of Things. Until now, existing works generally focus on artificial extraction of statistical features or applying deep learning to extract deep features to perform activity recognition. However, most studies neglect individual differences among different users, which leads to performance decline of the trained model when applied to new users. In this paper, we propose a novel approach using hypergraph learning for personalized human activity recognition based on fusion features. Fusion features take advantage of deep features and statistical features, and also fuse the user's personalization factors to reduce the influence of individual differences. In the classification part, a hypergraph learning algorithm is used to recognize user's activities based on the fusion features. Experiments on the public dataset USC-HAD (11-class) and the self-collected dataset (6-class) show the proposed method has superior performance compared to existing methods, as well as greater potential for usage in personalized human activity recognition during daily lives.