Integration of human walking gyroscopic data using empirical mode decomposition.

Area of Science:

• Biomechanics
• Biomedical Engineering
• Signal Processing

Background:

• Wearable inertial measurement units (IMUs) are valuable for gait analysis but prone to drift in angular velocity signals.
• Accurate estimation of lower trunk 3D orientation is crucial for understanding gait dynamics and biomechanics.

Purpose of the Study:

• To evaluate the Empirical Mode Decomposition (EMD) method for estimating lower trunk 3D orientation from IMU data.
• To assess the efficacy of the EMD method in correcting drift in angular velocity signals during walking.

Main Methods:

• An IMU was mounted on the lower trunk (L4-L5) to capture angular velocity signals.
• The Empirical Mode Decomposition (EMD) method was applied offline to detrend integrated angular velocities.
• The method was validated against stereophotogrammetric data in two groups of subjects during overground and treadmill walking.

Main Results:

• The IMU/EMD method successfully detrended integrated angular velocities, mitigating drift.
• Accurate estimation of lateral bending, flexion-extension, and axial rotations was achieved.
• Root-mean-square errors were 1° for straight walking and below 2.5° for walking with turns.

Conclusions:

• The IMU/EMD method provides a parameter-free, effective approach for estimating lower trunk 3D orientation during walking.
• This technique offers a reliable solution for gait analysis using IMU data, even with inherent sensor drift.