| Autor: |
Ruizhi Chen, Heidi Kuusniemi, Yuwei Chen, Robert Guinness, Jingbin Liu, Ling Pei |
| Jazyk: |
angličtina |
| Rok vydání: |
2012 |
| Předmět: |
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| Zdroj: |
Sensors, Vol 12, Iss 5, Pp 6155-6175 (2012) |
| Druh dokumentu: |
article |
| ISSN: |
1424-8220 |
| DOI: |
10.3390/s120506155 |
| Popis: |
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”. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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