IMU/UWB Fusion Method Using a Complementary Filter and a Kalman Filter for Hybrid Upper Limb Motion Estimation.

Area of Science:

• Human-Computer Interaction
• Aerospace Engineering
• Robotics
• Biomechanics

Background:

• Optical motion capture systems are costly and sensitive to lighting.
• Inertial Measurement Unit (IMU) sensors suffer from drift.
• Existing systems face limitations in cost, environmental sensitivity, and accuracy.

Purpose of the Study:

• To develop a hybrid, low-cost motion tracking system for upper limb movements.
• To fuse data from IMU and Ultrawideband (UWB) sensors for enhanced accuracy.
• To overcome the limitations of existing motion capture technologies in aerospace applications.

Main Methods:

• Proposed a novel algorithm combining the fourth-order Runge-Kutta (RK4) Madgwick complementary orientation filter and a Kalman filter.
• Utilized data fusion from an Inertial Measurement Unit (IMU) and an Ultrawideband (UWB) system.
• Employed Madgwick RK4 filter to compensate gyroscope drift and a Kalman filter to fuse UWB measurements for drift reduction.

Main Results:

• The proposed IMU/UWB fusion method demonstrated an average decrease in Root Mean Square Error (RMSE) of 1.2 cm compared to IMU-only methods.
• Achieved drift-free position estimation using UWB localization Kalman filter fused with IMU-calculated position.
• Experimental results confirmed high feasibility and stability for accurate human upper limb motion tracking.

Conclusions:

• The hybrid IMU/UWB motion tracking system offers a cost-effective and accurate solution for upper limb movement analysis.
• The proposed data fusion algorithm effectively mitigates sensor drift and enhances tracking precision.
• This technology holds significant potential for applications in human-computer interaction within the aerospace field and beyond.