Recognition of Human Actions using Layered Hidden Markov Models

Abstract

Human activity recognition has been a major goal of research in the field of human - computer interaction. This paper proposes a method which employs a hierarchical structure of Hidden Markov Models (Layered HMMs) in an attempt to exploit inherent characteristics of human action for more efficient recognition. The case study concerns actions of the arms of a seated subject and depends on the assumption of a static office environment. The first layer of HMMs detects short, primitive motions with direct targets, while every upper layer processes the previous layer inference to recognize abstract actions of longer time granularities. The results demonstrate the efficiency, the tolerance on noise interpolation and the high degree of person - invariance of the method.