Bayesian sensing hidden markov model for hand gesture recognition

Ari Hernawan, Yuan Shan Lee, Andri Santoso, Chien Yao Wang, Jia Ching Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper proposes a modified Bayesian Sensing Hidden Markov Model (BS-HMM) to address the problem of hand gestures recognition on few labeled data. In this work, BS-HMM is investigated based on its success to address the problem of largevocabulary of continuous speech recognition. We introduced error modeling into BS-HMM basis vector to handle the noise that occurs in the data. We also introduced a forgetting factor to preserve important information from previous basis vector and to improve both convergence and representation ability of the BS-HMM basis vector. We modified Moving Pose method to extract the feature descriptor from hand gestures data. To evaluate the performance of our system, we compared our proposed method with previously proposed HMM methods. The experimental result showed the improvement of proposed method over others, even when only a small number of labeled data are available for training dataset.

Original languageEnglish
Title of host publicationProceedings of the ASE BigData and SocialInformatics 2015, ASE BD and SI 2015
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450337359
StatePublished - 7 Oct 2015
EventASE BigData and SocialInformatics, ASE BD and SI 2015 - Kaohsiung, Taiwan
Duration: 7 Oct 20159 Oct 2015

Publication series

NameACM International Conference Proceeding Series


ConferenceASE BigData and SocialInformatics, ASE BD and SI 2015


  • Bayesian sensing hidden Markov models
  • Hand gesture recognition
  • Moving pose descriptor


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