Adaptive sensor data fusion in motion capture

Micro-sensor human motion capture has shown its potentials because of its ubiquity and low cost. One of the biggest challenges in micro-sensor motion estimation is the drift problem caused by integration of angular rates to obtain orientation. To reduce the drift, existing algorithms make use of gravity and earth magnetic filed measured by accelerometers and magnetometers respectively. Unfortunately, body segment acceleration and environment magnetic disturbance produce strong interferences to the gravity and earth magnetic field measurement respectively. This paper presents a novel sensor fusion algorithm for drift-free orientation estimation, where a quaternion-based complementary Kalman filter is designed. To optimize the performance under interference, this filter fuses gyroscope, accelerometer and magnetometer signals adaptively based on their information confidence, which are evaluated by computing their interference level. The proposed algorithm showed least error compared with the existing methods in the quantitative experiments, and its effectiveness was also verified by the stable and accurate human motion estimation.