%0 Journal Article
%T Compound Hidden Markov Model for  Activity Labelling
%A Jose Israel Figueroa-Angulo
%A Jesus Savage
%A Ernesto Bribiesca
%A Boris Escalante
%A Luis Enrique Sucar
%J International Journal of Intelligence Science
%P 177-195
%@ 2163-0356
%D 2015
%I Scientific Research Publishing
%R 10.4236/ijis.2015.55016
%X This research presents a novel way of
labelling human activities from the skeleton output computed from RGB-D data
from vision-based motion capture systems. The activities are labelled by means
of a Compound Hidden Markov Model. The linkage of several Linear Hidden Markov
Models to common states, makes a Compound Hidden Markov Model. Each separate
Linear Hidden Markov Model has motion information of a human activity. The
sequence of most likely states, from a sequence of observations, indicates
which activities are performed by a person in an interval of time. The purpose
of this research is to provide a service robot with the capability of human
activity awareness, which can be used for action planning with implicit and
indirect Human-Robot Interaction. The proposed Compound Hidden Markov Model,
made of Linear Hidden Markov Models per activity, labels activities from
unknown subjects with an average accuracy of 59.37%, which is higher than the
average labelling accuracy for activities of unknown subjects of an Ergodic
Hidden Markov Model (6.25%), and a Compound Hidden Markov Model with activities
modelled by a single state (18.75%).
%K Hidden Markov Model
%K Compound Hidden Markov Model
%K Activity Recognition
%K Human Activity
%K Human Motion
%K Motion Capture
%K Skeleton
%K Computer Vision
%K Machine Learning
%K Motion Analysis
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=60179