Biomechanical model-based multi-sensor motion estimation

Abstract

Motion estimation drift has been a challenge in inertial sensor motion capture research. This paper presents a novel biomechanical model-based multi-sensor motion estimation method working on a group of sensor units attached to a limb. In this method, biomechanical model provides constraints and defines relationships among sensors. The motion parameters of neighboring segments are estimated together by using unscented Kalman filter with those constraints and relationships. The performance of this method is benchmarked through the optical/inertial combined capture experiments. The experiment results show that our algorithm increases the accuracy of motion estimation.