Research note on gravity artifact reduction in IMU signals using Madgwick orientation estimation

Gravitational artifacts bias Inertial Measurement Unit (IMU) signals, complicating applications such as Seismocardiography (SCG). This study aimed to validate a gravity-artifact reduction algorithm using Madgwick orientation estimation in a controlled, physics-based simulation.

A computational phantom was developed using the PyBullet physics engine to generate paired datasets of gravity-free acceleration and simulated IMU signals (with gravitational artifacts). The Madgwick Attitude and Heading Reference System (AHRS) filter was applied to estimate the sensor orientation, followed by a low-pass-filtered gravity projection to subtract artifacts. The gravity-removed acceleration closely matched the gravity-free reference across all axes. Absolute mean differences were consistently below 0.006 m/s², and Pearson correlation coefficients exceeded 0.98. Cross-correlation analysis revealed zero temporal lag, while Bland-Altman analysis showed no proportional bias. The highest precision was observed along the gravitational z-axis. These simulation-only results represent an upper bound under idealized conditions. Experimental validation remains necessary before practical deployment.