Using LS-SVM based motion recognition for smartphone indoor wireless positioning.
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Date
2012-07-10
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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
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Keywords
Client, Access to information or data, Prototype, Data collection and reporting, Surveillance, Accelerometers / Motion sensors, Installed application, Raw data