A Tightly Coupled IMU-Based Motion Capture Approach for Estimating Multibody Kinematics and Kinetics

2025-05-13

AI Summary

This paper addresses key challenges—magnetic interference, gyroscope drift, and model inconsistency—in inertial measurement unit (IMU)-based multi-body motion capture (MoCap) for clinical and home environments. We propose a magnetometer-free tightly coupled estimation framework that deeply fuses accelerometer and gyroscope measurements with a multi-body dynamics model. Using an iterated extended Kalman filter (IEKF), the method jointly estimates kinematic states (joint angles) and dynamic states (joint torques), while explicitly enforcing kinematic–dynamic consistency constraints. It further supports sensor fusion with optical MoCap systems and encoders. To our knowledge, this is the first work achieving tight coupling between pure IMU measurements and a multi-body dynamics model. Experimental validation shows root-mean-square deviations (RMSDs) of 3.75° in joint angles for a 3-DoF pendulum system (versus optical inverse kinematics) and 3.24° for a 6-DoF KUKA robot (versus encoder ground truth); corresponding joint torque RMSDs are 2.0 Nm and 3.73 Nm.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Inertial Measurement Units (IMUs) enable portable, multibody motion capture (MoCap) in diverse environments beyond the laboratory, making them a practical choice for diagnosing mobility disorders and supporting rehabilitation in clinical or home settings. However, challenges associated with IMU measurements, including magnetic distortions and drift errors, complicate their broader use for MoCap. In this work, we propose a tightly coupled motion capture approach that directly integrates IMU measurements with multibody dynamic models via an Iterated Extended Kalman Filter (IEKF) to simultaneously estimate the system's kinematics and kinetics. By enforcing kinematic and kinetic properties and utilizing only accelerometer and gyroscope data, our method improves IMU-based state estimation accuracy. Our approach is designed to allow for incorporating additional sensor data, such as optical MoCap measurements and joint torque readings, to further enhance estimation accuracy. We validated our approach using highly accurate ground truth data from a 3 Degree of Freedom (DoF) pendulum and a 6 DoF Kuka robot. We demonstrate a maximum Root Mean Square Difference (RMSD) in the pendulum's computed joint angles of 3.75 degrees compared to optical MoCap Inverse Kinematics (IK), which serves as the gold standard in the absence of internal encoders. For the Kuka robot, we observe a maximum joint angle RMSD of 3.24 degrees compared to the Kuka's internal encoders, while the maximum joint angle RMSD of the optical MoCap IK compared to the encoders was 1.16 degrees. Additionally, we report a maximum joint torque RMSD of 2 Nm in the pendulum compared to optical MoCap Inverse Dynamics (ID), and 3.73 Nm in the Kuka robot relative to its internal torque sensors.

Problem

• Estimating multibody kinematics and kinetics using IMUs
• Addressing IMU measurement challenges like drift and distortions
• Enhancing accuracy by integrating IMU data with dynamic models

Innovation

• Tightly couples IMU with multibody dynamics via IEKF
• Enforces kinematic and kinetic properties for accuracy
• Allows integration of additional sensor data flexibly