A quaternion-based orientation estimation algorithm with adaptive interpolation for wearable computing

The motion capture systems that have rigorous real time requirements are often applied in wearable computing, aerospace and other fields. This paper presents a quaternion-based Orientation Estimation Algorithm (OEA) with low complexity using a Micro-Electro-Mechanic Systems (MEMS) tri-axis accelerometer, a tri-axis gyroscope and a tri-axis magnetometer. We give the description of the orientation of rigid body and propose an adaptive interpolation algorithm for data fusion. Experimental results demonstrate that the proposed OEA may track the orientation accurately and consume lower power as well as processing resources in comparison to the extended kalman filtering algorithm.