Author(s)

Jose Israel Figueroa-Angulo¹, Jesus Savage¹, Ernesto Bribiesca², Boris Escalante³, Luis Enrique Sucar⁴

¹Biorobotics Laboratory, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico.
²Computer Science Department, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico.
³Electrical Engineer Department, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico.
⁴Computer Science Department, Instituto Nacional de Astrofisica, Optica y Electronica, Puebla, Mexico.

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%).

Hidden Markov Model, Compound Hidden Markov Model, Activity Recognition, Human Activity, Human Motion, Motion Capture, Skeleton, Computer Vision, Machine Learning, Motion Analysis

Figueroa-Angulo, J. , Savage, J. , Bribiesca, E. , Escalante, B. and Sucar, L. (2015) Compound Hidden Markov Model for Activity Labelling. International Journal of Intelligence Science, 5, 177-195. doi: 10.4236/ijis.2015.55016.