LMA-based human behaviour analysis using HMM

Kamrad Khoshhal, Hadi Aliakbarpour, Kamel Mekhnacha, Julien Ros, Joao Quintas, Jorge Dias

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

6 Scopus citations


In this paper a new body motion-based Human Behaviour Analysing (HBA) approach is proposed for the sake of events classification. Here, the interesting events are as normal and abnormal behaviours in a Automated Teller Machine (ATM) scenario. The concept of Laban Movement Analysis (LMA), which is a known human movement analysing system, is used in order to define and extract sufficient features. A two-phase probabilistic approach have been applied to model the system's state. Firstly, a Bayesian network is used to estimate LMA-based human movement parameters. Then the sequence of the obtained LMA parameters are used as the inputs of the second phase. As the second phase, the Hidden Markov Model (HMM), which is a well-known approach to deal with the time-sequential data, is used regarding the context of the ATM scenario. The achieved results prove the eligibility and efficiency of the proposed method for the surveillance applications.

Original languageBritish English
Title of host publicationTechnological Innovation for Sustainability - Second IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2011, Proceedings
Number of pages10
StatePublished - 2011

Publication series

NameIFIP Advances in Information and Communication Technology
Volume349 AICT
ISSN (Print)1868-4238


  • HMM and Bayesian Network
  • Human Behaviour Analysing
  • Laban Movement Analysis


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