A bag-of-features-based framework for human activity representation and recognition

Abstract

Human activity recognition using wearable sensors is an important topic in ubiquitous computing. In this paper, we present a statistical motion primitive-based framework for human activity representation and recognition. Our framework is based on Bag-of-Features (BoF), which builds activity models using histograms of primitive symbols. Experimental results validate the effectiveness of this framework for the task of human activity recognition. In addition, we have demonstrated that our statistical BoF framework can achieve a much better performance compared to the non-statistical string-matching-based approach.