Abstract
A distributed real-time system, based on wearable accelerometers and magnetic sensors, is proposed for location estimation and recognition of walking behaviors. Suitable for both outdoor and indoor navigation, the system is especially adjusted for irregular movements indoors. The algorithm, which demands only small computing resources, performs step detection and classification in the time domain, allowing the estimation of the size of each separate step independently. Since the system finds the user’s position relative to an initial position, it is intended to be supplemented with different types of absolute positioning information. Making use of map knowledge, as an easily available source of this information, is analyzed. The conclusion is drawn that referring to the locations of the corridors and stairways increases the positioning accuracy and reduces the effect of magnetic field distortions encountered inside buildings. The positioning error of different system configurations was 3-10 % from traveled distance.
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Vildjiounaite, E., Malm, EJ., Kaartinen, J., Alahuhta, P. (2002). Location Estimation Indoors by Means of Small Computing Power Devices, Accelerometers, Magnetic Sensors, and Map Knowledge. In: Mattern, F., Naghshineh, M. (eds) Pervasive Computing. Pervasive 2002. Lecture Notes in Computer Science, vol 2414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45866-2_17
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DOI: https://doi.org/10.1007/3-540-45866-2_17
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