Indoor positioning system based on inertial MEMS sensors: Design and realization

Nowadays, location-based services (LBS) has become widely used in our daily life. The most famous system is global positioning system (GPS), which is limited to outdoor applications and provide poor locating accuracy. In this paper, we present a positioning systems based on inertial MEMS sensor which includes three-axis accelerometer, three-axis gyroscope and three-axis magnetometer. The system can help people get accurate positioning for indoor environments, also available for outdoors, because of its self-contained character. It is a foot wearable device with wireless network to transmit movement information to computer that can calculate the relative position and show the path walked by. The key concept of the positioning system is inertial navigation and dead reckoning technology. Since it needs twice-integration of the acceleration to get the position, the displacement will drift by time elapse. We make it only drift by distance increasing through gait phase analysis, a method called Zero-Velocity Update (ZVU). As the “stand-still phase” is the key of the system performance, we mainly focus on getting accurate gait phase detection. We used decision tree here and the experimental results showed that we got a gait phase detection accuracy of 99.96% and positioning accuracy of 97.37%.

[1]  A. Hof,et al.  Displacement of the pelvis during human walking: experimental data and model predictions , 1997 .

[2]  Adrian Schumacher,et al.  Integration of a GPS aided Strapdown Inertial Navigation System for Land Vehicles , 2006 .

[3]  Xiaoping Yun,et al.  Self-contained Position Tracking of Human Movement Using Small Inertial/Magnetic Sensor Modules , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[4]  Fernando Seco Granja,et al.  Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[5]  Tamal Mukherjee,et al.  A Low-Power Shoe-Embedded Radar for Aiding Pedestrian Inertial Navigation , 2010, IEEE Transactions on Microwave Theory and Techniques.

[6]  Sebastian Madgwick,et al.  Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[7]  Chadly Marouane,et al.  Indoor positioning using smartphone camera , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[8]  R. Mautz Indoor Positioning Technologies , 2012 .

[9]  Tieniu Tan,et al.  A cascade fusion scheme for gait and cumulative foot pressure image recognition , 2012, Pattern Recognit..

[10]  Robert B. McGhee,et al.  Estimation of Human Foot Motion During Normal Walking Using Inertial and Magnetic Sensor Measurements , 2012, IEEE Transactions on Instrumentation and Measurement.