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.