Ariadne's thread: Robust turn detection for path back-tracing using the iPhone

Abstract

Most systems for pedestrian localization and self-tracking aim to measure the precise position of the walker and match it against a map of the environment. In some cases, a simpler topological description of the path taken may suffice. This is the case for the system described in this paper, which is designed to help a blind person re-trace the route taken inside a building and to walk safely back to the starting point. We present two turn detection algorithms based on hidden Markov models (HMM), which process inertial data collected by an iPhone kept in the walker's front pocket, without the need for a map of the environment. Quantitative results show the robustness of the proposed turn detectors even in the case of drift in the measurements and noticeable body sway during gait.