Guenterberg, E., Ghasemzadeh, H., Loseu, V. and Jafari, R. (2009) Distributed Continuous Action Recognition Using a Hidden Markov Model in Body Sensor Networks. In: Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems, Springer-Verlag, Berlin, Heidelberg, 145-158.
http://dx.doi.org/10.1007/978-3-642-02085-8_11

has been cited by the following article:

• TITLE: Compound Hidden Markov Model for Activity Labelling

  AUTHORS: Jose Israel Figueroa-Angulo, Jesus Savage, Ernesto Bribiesca, Boris Escalante, Luis Enrique Sucar

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

  JOURNAL NAME: International Journal of Intelligence Science, Vol.5 No.5, October 9, 2015

  ABSTRACT: 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%).