Using Motion-Awareness for the 3D Indoor Personal Navigation on a Smartphone

Ling Pei, Ruizhi Chen, Jingbin Liu, Heidi Kuusniemi, Yuwei Chen, Tomi Tenhunen

Abstract:	The paper presents an indoor navigation solution combining physical motion recognition with wireless positioning in a three dimensional space. 27 features are extracted utilizing the built-in accelerometers and magnetometers in a smartphone. 8 common motion modes during indoor navigation, e.g., static, standing with hand swinging, normal walking with holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs are detected by the three classification algorithms: Bayesian Network (BN), Decision Tree (DT), and Support Vector Machine (SVM) respectively. Test results indicate that the motion modes are recognized correctly up to 95.53% of test cases. A motion-awareness assisted wireless positioning approach is applied to determine the position of a smartphone user. The field tests show 1.22 m mean error in the “Static Test” and 3.53 m in the “Stop-Go Test”.
Published in:	Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011) September 20 - 23, 2011 Oregon Convention Center, Portland, Oregon Portland, OR
Pages:	2906 - 2913
Cite this article:	Pei, Ling, Chen, Ruizhi, Liu, Jingbin, Kuusniemi, Heidi, Chen, Yuwei, Tenhunen, Tomi, "Using Motion-Awareness for the 3D Indoor Personal Navigation on a Smartphone," Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011), Portland, OR, September 2011, pp. 2906-2913.
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