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

Content Provider	IEEE Xplore Digital Library
Author	Syed, Y.A. Brown, D.J. Garrity, D. Mackinnon, A.
Copyright Year	2015
Description	Author affiliation: STS Defence Ltd., Gosport, UK (Garrity, D.; Mackinnon, A.) \|\| Inst. of Ind. Res., Univ. of Portsmouth, Portsmouth, UK (Syed, Y.A.; Brown, D.J.)
Abstract	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.
Starting Page	1
Ending Page	6
File Size	476119
Page Count	6
File Format	PDF
e-ISBN	9781479976263
DOI	10.1109/NSITNSW.2015.7176411
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-02-17
Publisher Place	Saudi Arabia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accelerometers Naïve Bayes Magnetometers Indoor navigation Pedestrian dead reckoning HMM Accuracy Hidden Markov models Inertial navigation Kalman filtering Sensors Acceleration Kalman filters
Content Type	Text
Resource Type	Article

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