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Exercise Recognition Using Averaged Hidden Markov Models

  • Aleksandra PostawkaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10246)

Abstract

This paper presents a novel learning algorithm for Hidden Markov Models (HMMs) based on multiple learning sequences. For each activity a few left-to-right HMMs are created and then averaged into singular model. Averaged models’ structure is defined by a proposed Sequences Concatenation Algorithm which has been included in this paper. Also the modification of action recognition algorithm for such averaged models has been described.

The experiments have been conducted for the problem of modeling and recognition of chosen 13 warm-up exercises. The input data have been collected using the depth sensor Microsoft Kinect 2.0. The experiments results confirm that an averaged model combines the features of all component models and thus recognizes more sequences. The obtained models do not confuse modeled activities with others.

Keywords

Action recognition Left-to-right Hidden Markov Models Kinect Averaged models 

Notes

Acknowledgment

This work was supported by the statutory funds of the Faculty of Electronics 0402/0104/16, Wroclaw University of Science and Technology, Wroclaw, Poland.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of ElectronicsWroclaw University of Science and TechnologyWroclawPoland

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