Force Feedback-Based Adaptive Sensor Fusion Algorithm for Precision Enhancement in VR-Based Medical Training Systems

Sub-millimeter tracking accuracy is an important requirement for high-fidelity simulation in Virtual Reality (VR)-based medical training systems. Conventional optical tracking systems suffer from significant errors during occlusion, while standalone Inertial Measurement Unit (IMU) usage leads to severe drift accumulation. This study proposes an Adaptive Extended Kalman Filter (AEKF)-based sensor fusion algorithm that integrates optical tracking, IMU, and force/torque sensors. The key technical contributions are: (1) a force feedback-based position drift correction mechanism, (2) innovation-based adaptive process noise adjustment, and (3) real-time tissue stiffness estimation through a 21-dimensional state vector. To evaluate the proposed algorithm’s performance, we generated simulation datasets for three standard medical scenarios (laparoscopic suturing, catheter insertion, and needle biopsy) and quantitatively compared five sensor fusion algorithms: (A) optical-only, (B) IMU-only, (C) standard EKF (optical+IMU), (D) Unscented KF (optical+IMU), and (E) proposed method (optical+IMU+force). Simulation-based experiments show that the proposed algorithm achieves an average position tracking error of 0.75mm (±0.19mm) in controlled simulation conditions, representing 95.5% improvement over optical-only (Pp< 0.01) within the simulation environment. Furthermore, it maintains stable performance below 1.2mm even during simulated optical occlusion and achieves computational latency of 2.1ms on the tested hardware platform. This research focuses on algorithmic development and simulation-based validation, and does not include hardware prototype implementation, user studies with medical professionals, or clinical validation. The results demonstrate algorithmic feasibility under idealized simulation conditions rather than validated performance in real clinical environments.