Non-linear complementary filter based upper limb motion tracking using wearable sensors

In this paper, we present a method to reconstruct motion trajectories of the upper body using inertial measurement units (IMUs). We combine the use of complementary filters and biomechanical models to reconstruct upper body motions. At rst, we use complementary lters to combine information from low-frequency part of accelerometers and magnetometers, and high-frequency part of gyros to estimate sensor orientations and gyro bias. Then we use the estimated orientations of the upper arm and forearm to calculate trajectories of upper limb movements. Finally, we determine the set of parameters for complementary lters that minimized training errors. Experimental results indicate higher than 90% accuracy using accelerometers, gyros and magnetometers.