Region-Selective Synthetic Data Injection for Data-Driven Magnetic Capsule Pose Estimation

Abstract

Accurate Wireless Magnetic Capsule (WCE) pose estimation remains a challenge for advancing minimally invasive medical procedures, because the relationship between magnetic sensor measurements and capsule pose is highly nonlinear and sensitive to noise and modeling errors, making large-scale training data essential for data-driven estimation. However, data acquisition itself remains a limiting factor, restricting both the volume of training data and the effective workspace of the system. To address this limitation, we propose a region-selective synthetic data injection strategy that generates additional data points using a calibrated physics-based model. In this strategy, regions with high model fidelity are replaced with physics-based data at arbitrary points, while regions with lower fidelity rely on sensor data, which provides a more accurate representation of the real system. Experimental results show that the proposed strategy achieves performance comparable to that of a purely data-driven model while significantly reducing the data acquisition burden.