Abstract
More than half of the population in Western Europe and North America own a smartphone, providing a large market for both indoor and outdoor location-based services. In order to gain a ubiquitous solution for a smartphone-based indoor positioning, motion recognition may be utilized. Motion recognition can be used to adapt relative positioning solutions as well as the position filtering process. The presented motion recognition is based on classic machine learning techniques, filtered within the time and motion domain to gain a more robust estimation. The outcome of the motion recognition is used within a Pedestrian Dead Reckoning (PDR) algorithm as well as in a particle filter, but is especially helpful within the step detection process of the PDR. Within the step length estimation of PDR, the step length is strongly overestimated when walking on stairs. Contrary, when walking fast, the step length is underestimated by standard step length models. This estimation can be improved using motion recognition.
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