%0 Journal Article
%T Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning
%A Ling Pei
%A Jingbin Liu
%A Robert Guinness
%A Yuwei Chen
%A Heidi Kuusniemi
%A Ruizhi Chen
%J Sensors
%D 2012
%I MDPI AG
%R 10.3390/s120506155
%X 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 ˇ°Static Testsˇ± and a 3.53 m in ˇ°Stop-Go Testsˇ±.
%K motion recognition
%K LS-SVM
%K indoor navigation
%K positioning
%K wireless
%K smartphone
%U http://www.mdpi.com/1424-8220/12/5/6155