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A hidden Markov model-based activity classifier for indoor tracking of first responders

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Yusuf A. Syed
  • David J. Brown
  • David Garrity
  • Alan Mackinnon

Pedestrian navigation via dead reckoning (PDR) is considered a promising domain for search and rescue personnel tracking, particularly for fire-fighters. The technique is considered particularly useful when other conventional means such as the GPS and RF-based location estimation are not present or not accurate. However, PDR approaches in real-world operating environments fail due to a wide range of factors ranging from the personnel's natural behavior to diversity of activities a first-responder may perform during a rescue mission. This technique presents a PDR activity classification technique utilizing shoe-mounted microelectromechanical sensors for efficient step and attitude analysis via a 2D Kalman filter. The methodology then utilizes HMMs for various activity types such as walking, side-stepping, crawling, etc. Tests performed on the proposed technique showed the step identification technique to perform well with an overall accuracy of 90.75% in step-counting where a simple Naïve Bayes classifier was used. The HMM-based activity classifier presented 86% and 85% accuracy in correctly identifying upstairs and downstairs walking activity.

Original languageEnglish
Title of host publication2015 5th National Symposium on Information Technology
Subtitle of host publicationTowards New Smart World, NSITNSW 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)978-1479976263
Publication statusPublished - 3 Aug 2015
Event5th National Symposium on Information Technology: Towards New Smart World, NSITNSW 2015 - Riyadh, Saudi Arabia
Duration: 17 Feb 201519 Feb 2015


Conference5th National Symposium on Information Technology: Towards New Smart World, NSITNSW 2015
CountrySaudi Arabia

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