Displacement estimation for different gait patterns in micro-sensor motion capture

The human body displacement estimation in different gait patterns using wearable sensors is extremely challenging due to lack of external references. In this paper, we present a novel algorithm to estimate the Center of Mass (CoM) displacement of human body during walking, running and hopping using 7 body-worn Sensor Measurement Units (SMUs). The lower body posture and feet displacements are firstly estimated by a complementary Kalman filter (CKF) which compensates the orientation, velocity and position errors of the Inertial Navigation system (INS) solutions through its error state vector. The CoM displacement can then be acquired by further fusion of the lower body posture and feet locations based on the linked biomechanical model. The experimental results have shown that our method can accurately capture human motion including orientation and locomotion for these three different gait patterns with regard to the optical motion tracker.