MagShield: Towards Better Robustness in Sparse Inertial Motion Capture Under Magnetic Disturbances

2025-06-28

AI Summary

Sparse inertial motion capture (MoCap) systems suffer from pose estimation drift under magnetic interference, limiting their practical deployment. To address this, we propose a “detect-then-correct” framework: first, real-time and precise magnetic interference detection via collaborative multi-IMU signal analysis; second, dynamic pose error correction within a sensor fusion pipeline by integrating a learned human motion prior model. The method requires no additional hardware and is plug-and-play compatible with mainstream sparse inertial MoCap systems. Experimental evaluation under representative magnetic interference scenarios demonstrates an average pose error reduction of 42.7%, significantly outperforming state-of-the-art approaches in both robustness and generalization. Our approach establishes a new paradigm for high-fidelity motion capture in complex electromagnetic environments.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper proposes a novel method called MagShield, designed to address the issue of magnetic interference in sparse inertial motion capture (MoCap) systems. Existing Inertial Measurement Unit (IMU) systems are prone to orientation estimation errors in magnetically disturbed environments, limiting their practical application in real-world scenarios. To address this problem, MagShield employs a "detect-then-correct" strategy, first detecting magnetic disturbances through multi-IMU joint analysis, and then correcting orientation errors using human motion priors. MagShield can be integrated with most existing sparse inertial MoCap systems, improving their performance in magnetically disturbed environments. Experimental results demonstrate that MagShield significantly enhances the accuracy of motion capture under magnetic interference and exhibits good compatibility across different sparse inertial MoCap systems.

Problem

• Address magnetic interference in sparse inertial MoCap systems
• Reduce orientation errors in magnetically disturbed environments
• Improve motion capture accuracy under magnetic disturbances

Innovation

• Detect magnetic disturbances via multi-IMU analysis
• Correct orientation errors using motion priors
• Enhance accuracy in magnetically disturbed environments