Using LS-SVM based motion recognition for smartphone indoor wireless positioning.

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dc.contributor.authorPei, Ling
dc.contributor.authorLiu, Jingbin
dc.contributor.authorGuinness, Robert
dc.contributor.authorChen, Yuwei
dc.contributor.authorKuusniemi, Heidi
dc.contributor.authorChen, Ruizhi
dc.date.accessioned2020-02-06T17:38:55Z
dc.date.available2020-02-06T17:38:55Z
dc.date.issued2012-07-10
dc.description.abstractThe 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
dc.identifier.urihttp://dx.doi.org/10.3390/s120506155
dc.identifier.urihttps://lib.digitalsquare.io/xmlui/handle/123456789/5414
dc.relation.uriSensors (Basel, Switzerland)
dc.subjectClient
dc.subjectAccess to information or data
dc.subjectPrototype
dc.subjectData collection and reporting
dc.subjectSurveillance
dc.subjectAccelerometers / Motion sensors
dc.subjectInstalled application
dc.subjectRaw data
dc.titleUsing LS-SVM based motion recognition for smartphone indoor wireless positioning.en
dcterms.abstractThe 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
dcterms.contributorPei, Ling
dcterms.contributorLiu, Jingbin
dcterms.contributorGuinness, Robert
dcterms.contributorChen, Yuwei
dcterms.contributorKuusniemi, Heidi
dcterms.contributorChen, Ruizhi
dcterms.identifierhttp://dx.doi.org/10.3390/s120506155
dcterms.relationSensors (Basel, Switzerland)
dcterms.subjectClient
dcterms.subjectAccess to information or data
dcterms.subjectPrototype
dcterms.subjectData collection and reporting
dcterms.subjectSurveillance
dcterms.subjectAccelerometers / Motion sensors
dcterms.subjectInstalled application
dcterms.subjectRaw data
dcterms.titleUsing LS-SVM based motion recognition for smartphone indoor wireless positioning.en
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