Department of Navigation and Positioning, Finnish Geodetic Institute, FIN-02431 Masala, Finland.
Sensors (Basel). 2012;12(5):6155-75. doi: 10.3390/s120506155. Epub 2012 May 10.
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".
本文提出了一种将物理运动识别与无线定位相结合的室内导航解决方案。从智能手机内置的加速度计和磁力计中提取了 27 个简单特征。通过最小二乘支持向量机（LS-SVM）分类算法检测到 8 种常见的室内导航运动状态，例如静止、手持摆动站立、手持手机正常行走、手持摆动正常行走、快走、U 型转弯、上下楼梯。结果表明，在所研究的测试案例中，运动状态的识别准确率高达 95.53%。运动识别辅助无线定位方法用于确定移动用户的位置。现场测试表明，“静态测试”的平均误差为 1.22 米，“走走停停测试”的平均误差为 3.53 米。
