Activity and environment classification using foot mounted navigation sensors

Foot mounted navigation systems can be deployed for tracking military personnel, first responders and offenders. Determining the activity and environment of individuals can provide valuable information to those monitoring these individuals. This paper provides activity and environment classification for a foot mounted device that uses an IMU and GPS receiver. Using information from the navigation filter (e.g. velocity), GPS signal tracking parameters, and IMU measurements this paper presents an algorithm that classifies the following activities: indoor, outdoor, stationary, crawling, walking, running, biking, moving in vehicle, level, up or down elevator and up or down stairs. Multiple probability density functions that map each feature (i.e. metric) to an activity are provided. Then a naive Bayesian probabilistic model is used to determine the probability of an activity. To improve reliability and accuracy of the classification several conditions are added. The algorithm shows excellent results for activity classification, however environment classification is less reliable due to variations in GPS tracking abilities as a function of the environment. Results are shown with images from the data collection.