Micro-IMU-based motion tracking system for virtual training

This paper presents the development of a low cost wireless real-time inertial body tracking system for virtual training. The system is designed to provide highly accurate human body motion capture and interactive three-dimensional (3-D) avatar steering, by combining low cost MEMS inertial measurement units (IMUs), wireless body sensor network (BSN), and Unity 3D virtual reality game engine. First, several wearable MEMS IMU sensors are placed on user's body and limbs according to human skeletal action, and each sensor performs a 9 degrees of freedom (DOF) tracking at a high-speed update rate. Second, a Zigbee-based BSN is designed to support up to 20 MEMS IMU sensor nodes data transmission at 50 Hz sampling frequency. All collected sensors' data are loaded to a Matlab-based PC program by means of serial port. In order to accurately estimate the local orientation of each IMU sensor, an optimized gradient descent algorithm is implemented. The algorithm uses a quaternion representation, which allows accelerometer and magnetometer data to be fused to compute the gyroscope measurement error as a quaternion derivative. Finally, the estimated orientation data by fusion algorithm are imported to a virtual environment, consisting of the 3-D virtual skeletal representation and the virtual scene for specific training. Experimental results indicate that the system achieves <; 1
^o
static RMS error and <;2
^o
dynamic RMS error. The systems further expand the usability of low cost body tracking solution to virtual training in virtual environments.