Bi-View Semi-Supervised Learning Based Semantic Human Activity Recognition Using Accelerometers

摘要

Semantic human activity (SHA) refers to users' activities performed in their daily lives (e.g., having dinner, shopping, etc.). SHA recognition is a promising issue in wearable and mobile computing. Most existing methods represent a SHA based on a single view, e.g., representing a SHA as a combination of human body actions, representing a SHA as a distribution of latent semantics. Since SHAs are complicated in nature, single views lack the ability of comprehensively profiling SHAs. In this paper, we propose a bi-view semi-supervised learning based method for recognizing SHAs using accelerometers. First, we represent a SHA based on two different views. One view represents a SHA as a distribution of latent activities in an unsupervised manner, and the other view represents a SHA as a set of human crafted features extracted in a hierarchical way. Second, we use a semi-supervised learning framework, which exploits the complementary information provided by the two views, to improve the classification accuracy based on both labeled and unlabeled data. Extensive experiments show that representing SHAs based on bi-views is more effective than representing SHAs based on single views, and our method is able to yield a competitive SHA recognition performance.