Milling State Identification Based on Vibration Sense of a Robotic Surgical System

Milling is one of the most popular forms of hard tissue removal process, it is sometimes high-risk because there may be very small distance between the milling trajectory and vital anatomy. This paper introduces a vibration signal acquiring and processing method to identify different types of milling states in robot-assisted orthopedics. During milling process, the tissue vibration signal measured by a laser displacement sensor, and the acceleration signal of the operation power device recorded by an accelerometer, are decomposed by the lifting wavelet packet transform, so the harmonic component whose frequency is an integer times of the spindle frequency of the operation power device is extracted. In consideration of that the tissue vibration amplitude varies with the milling force, the relative magnitude of the force is estimated from the acceleration signal, and then the wavelet energy of the tissue vibration signal is divided by that of the acceleration signal to reduce the influence of the force disturbance. The compensated wavelet energy is subsequently used as input vector to a support vector machine for discriminating different milling states. Experimental results on porcine spines demonstrate that the proposed method can successfully discriminate the tissues in the operation field: the success rate achieves 100% for the vertebra being milled and the spinal cord, for the adjacent bony structure and the muscle the success rate is about 90%.