Bayesian-based scenario generation method for human activities

Yunsick Sung, Abdelsalam Helal, Jae Woong Lee, Kyungeun Cho

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

8 Scopus citations

Abstract

Emerging smart space applications are increasingly relying on capabilities for recognizing human activities. Activity recognition research is however challenged and slowed by the lack of data necessary for testing and validation. Collecting data through live-in trials in real world deployments is often very expensive and complicated. Legitimate limitations on the use of human subjects also renders a much smaller dataset than desired to be collected. To address this challenge, we propose a scenario generation approach in which a small set of scenarios is used to generate new relevant and realistic scenarios, and hence increase the base of testing data needed for activity recognition validation. Unlike existing methods for generating scenarios, which usually focus on scenario structure and complexity, we propose a Bayesian-based approach that learns the stochastic characteristics of a small number of collected datasets to generate additional scenarios of similar characteristics. Our approach is prolific and can generate enormous datasets with high degree of realism at affordable cost. The proposed approach is validated using a Viterbi-based algorithm and a real dataset case study. The validation experiment confirms that the generated dataset has highly similar stochastic characteristics as that of the real dataset.

Original languageEnglish
Title of host publicationSIGSIM-PADS 2013 - Proceedings of the 2013 ACM SIGSIM Principles of Advanced Discrete Simulation
Pages147-157
Number of pages11
DOIs
StatePublished - 2013
Event2013 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, SIGSIM-PADS 2013 - Montreal, QC, Canada
Duration: 19 May 201322 May 2013

Publication series

NameSIGSIM-PADS 2013 - Proceedings of the 2013 ACM SIGSIM Principles of Advanced Discrete Simulation

Conference

Conference2013 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, SIGSIM-PADS 2013
Country/TerritoryCanada
CityMontreal, QC
Period19/05/1322/05/13

Keywords

  • activity recognition
  • bayesian probability
  • human activity
  • scenario generation

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