Real-Time Human Motion Capture Based on Wearable Inertial Sensor Networks

Wearable inertial motion capture, a new type of motion capture technology, mainly estimates the human posture in 3-D space through multi-sensor data fusion. The available method for sensor fusion are usually aided by magnetometers to remove the drift error in yaw angle estimation, which in turn limits their application in the presence of complex magnetic field environment. In this paper, an extended Kalman filter data fusion method is proposed to fuse the 9-axis sensor data. Meanwhile, heuristic drift reduction (HDR) method is used to calibrate the accumulated error of heading angle. In addition, the position in 3-D space is estimated by foot-mounted Zero-Velocity-Update (ZUPT) technique. Combining 3-D attitude and position, a biomechanical model of the human body is established to track the motion of real human body. The extended Kalman filter (EKF) algorithm and position estimation methods are benchmarked against the golden standard, optical motion capture system, for various indoor experiments. In addition, various outdoor experiments are also conducted to verify the reliability of the proposed method. The results show that the proposed algorithm outperforms the available attitude estimation model in motion tracking and is feasible for 3-D human motion capture.