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”.
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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.
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(5):6155-6175.
Pei, Ling; Liu, Jingbin; Guinness, Robert; Chen, Yuwei; Kuusniemi, Heidi; Chen, Ruizhi. 2012. "Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning." Sensors 12, no. 5: 6155-6175.