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Sensors 2012, 12(5), 6155-6175; doi:10.3390/s120506155

Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning

Department of Navigation and Positioning, Finnish Geodetic Institute, FIN-02431 Masala, Finland
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Received: 20 March 2012 / Revised: 17 April 2012 / Accepted: 28 April 2012 / Published: 10 May 2012
(This article belongs to the Section Physical Sensors)
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Abstract

The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in “Static Tests” and a 3.53 m in “Stop-Go Tests”.
Keywords: motion recognition; LS-SVM; indoor navigation; positioning; wireless; smartphone motion recognition; LS-SVM; indoor navigation; positioning; wireless; smartphone
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Pei, L.; Liu, J.; Guinness, R.; Chen, Y.; Kuusniemi, H.; Chen, R. Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning. Sensors 2012, 12, 6155-6175.

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