Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking

TLDR

Real‑time tracking of human body motion is a critical technology for synthetic environments, robotics, and human‑computer interaction applications. The paper presents an extended Kalman filter for real‑time estimation of human limb segment orientation. The filter employs quaternion representation, processes data from small inertial/magnetic sensor modules, and uses Quest‑algorithm preprocessing to reduce state dimension and linearize measurement equations. Experimental results validate the quaternion‑based Kalman filter and demonstrate that inertial/magnetic sensor modules enable real‑time human body motion tracking.

Abstract

Real-time tracking of human body motion is an important technology in synthetic environments, robotics, and other human-computer interaction applications. This paper presents an extended Kalman filter designed for real-time estimation of the orientation of human limb segments. The filter processes data from small inertial/magnetic sensor modules containing triaxial angular rate sensors, accelerometers, and magnetometers. The filter represents rotation using quaternions rather than Euler angles or axis/angle pairs. Preprocessing of the acceleration and magnetometer measurements using the Quest algorithm produces a computed quaternion input for the filter. This preprocessing reduces the dimension of the state vector and makes the measurement equations linear. Real-time implementation and testing results of the quaternion-based Kalman filter are presented. Experimental results validate the filter design, and show the feasibility of using inertial/magnetic sensor modules for real-time human body motion tracking