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

Authors:
Mi Zhang;Alexander A. Sawchuk
Affiliations:
University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, CA, USA
Venue:
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Year:
2011

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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.