Human Leg Motion Tracking by Fusing IMUs and RGB Camera Data Using Extended Kalman Filter

TL;DR

This paper introduces a quaternion-based Extended Kalman Filter method that fuses IMU sensors and RGB camera data to accurately track human leg motion, enhancing rehabilitation and animation applications.

Contribution

It presents a novel EKF approach that combines IMU and camera data for improved human leg motion tracking, utilizing a new measurement model for orientation and position estimation.

Findings

The proposed method accurately tracks leg motion using fused sensor data.

The algorithm outperforms traditional methods in motion capture accuracy.

Validation against optical motion tracker confirms effectiveness.

Abstract

Human motion capture is frequently used to study rehabilitation and clinical problems, as well as to provide realistic animation for the entertainment industry. IMU-based systems, as well as Marker-based motion tracking systems, are the most popular methods to track movement due to their low cost of implementation and lightweight. This paper proposes a quaternion-based Extended Kalman filter approach to recover the human leg segments motions with a set of IMU sensors data fused with camera-marker system data. In this paper, an Extended Kalman Filter approach is developed to fuse the data of two IMUs and one RGB camera for human leg motion tracking. Based on the complementary properties of the inertial sensors and camera-marker system, in the introduced new measurement model, the orientation data of the upper leg and the lower leg is updated through three measurement equations. The positioning of the human body is made possible by the tracked position of the pelvis joint by the camera marker system. A mathematical model has been utilized to estimate joints' depth in 2D images. The efficiency of the proposed algorithm is evaluated by an optical motion tracker system.