Inertial-Based Incision Depth Navigation Framework

Accurate estimation of incision depth is essential for surgical safety and optimal patient outcomes. However, current technologies rely on optical and magnetic tracking methods, which are costly and easily affected by environmental interference. In this research, we present a novel inertial-based navigation framework that enables real-time, millimeter-level incision depth estimation. Based on a single micro-electromechanical inertial measurement unit (MIMU) sensor and a constrained motion model, this framework can accurately track the incision depth of surgical scalpels. An optimized unit-quaternion-based square-root unscented Kalman filter (OUKF) method is proposed to enhance tracking robustness and precision. A low-cost navigation platform is developed and validated through simulation, phantom, and pre-clinical experiments. Results demonstrate that the system can provide accurate, real-time depth feedback and precise surgical guidance. With the integration of magnetic resonance imaging (MRI), the system successfully guided the surgeon to accurately expose the spinal cord of a rat. This research offers a promising technology for improving surgical precision, safety, and training across a wide range of clinical applications.