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A hidden Markov model-based activity classifier for indoor tracking of first responders
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.
A hidden Markov model-based activity classifier for indoor tracking of first responders
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.
A hidden Markov model-based activity classifier for indoor tracking of first responders
Syed, Yusuf A (author) / Brown, David J (author) / Garrity, David (author) / Mackinnon, Alan (author)
2015-02-01
476119 byte
Conference paper
Electronic Resource
English
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