Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2012-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/12/5/6155 |
| _version_ | 1798040162778742784 |
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| author | Ruizhi Chen Heidi Kuusniemi Yuwei Chen Robert Guinness Jingbin Liu Ling Pei |
| author_facet | Ruizhi Chen Heidi Kuusniemi Yuwei Chen Robert Guinness Jingbin Liu Ling Pei |
| author_sort | Ruizhi Chen |
| collection | DOAJ |
| description | 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”. |
| first_indexed | 2024-04-11T22:03:40Z |
| format | Article |
| id | doaj.art-e568b5b42e7a422f929825d159c48c7c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:03:40Z |
| publishDate | 2012-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e568b5b42e7a422f929825d159c48c7c2022-12-22T04:00:49ZengMDPI AGSensors1424-82202012-05-011256155617510.3390/s120506155Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless PositioningRuizhi ChenHeidi KuusniemiYuwei ChenRobert GuinnessJingbin LiuLing PeiThe 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”.http://www.mdpi.com/1424-8220/12/5/6155motion recognitionLS-SVMindoor navigationpositioningwirelesssmartphone |
| spellingShingle | Ruizhi Chen Heidi Kuusniemi Yuwei Chen Robert Guinness Jingbin Liu Ling Pei Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning Sensors motion recognition LS-SVM indoor navigation positioning wireless smartphone |
| title | Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning |
| title_full | Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning |
| title_fullStr | Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning |
| title_full_unstemmed | Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning |
| title_short | Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning |
| title_sort | using ls svm based motion recognition for smartphone indoor wireless positioning |
| topic | motion recognition LS-SVM indoor navigation positioning wireless smartphone |
| url | http://www.mdpi.com/1424-8220/12/5/6155 |
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