Improving pedestrian dynamics modelling using fuzzy logic

Summary. The complementary nature of MEMS based pedestrian dead-reckoning (PDR) navigation and GNSS (Global Navigation Satellite System) has long been recognised. The advantages are quite clear for those applications requiring in-door positioning and that, for one reason or another, cannot rely on short-range infrastructure-based positioning systems (e.g. WiFi, UWB) to cope with the lack of availability of GNSS indoors. One such example of application is firemen coor-dination during emergency interventions. Classification of human displacement using signal pattern recognition tech-niques often rely on an estimation model or statistical data to compute the step length or horizontal speed information. In general, an initial calibration phase is needed which can constrain the ability to follow the quasi-erratic behaviour of a pedestrian in real time. Moreover, existing state-of-the-art PDR solutions enable only the reconstruction of the 2D trajectory. This paper introduces a different approach to PDR navigation, in which pattern recognition is correlated to biomechanical principles and combined with fuzzy logic for detection and classification of a broader range of walking behaviours in 3D. Furthermore, to avoid the aforementioned limitations of stride length estimation, the step length is effectively computed by a simple inverse segment model during a specific phase of the gait cycle. Besides a description of the algorithm, this paper includes results of a real-time implementation capable of detecting/classifying four different types of steps: for-ward walk, stair climbing, stair descent forward and stair descent backward. This development has been conducted in the framework of the European project LIAI-SON [1, 2] funded by the Sixth Framework Program to specifically address one of its test case scenarios, the coordination of a fire brigade intervention.