A model of vertebral motion and key point recognition of drilling with force in robot-assisted spinal surgery

Abstract

Pedicle drilling is a crucial and high-risk process in spinal surgery. Due to the respiration and cardiac cycle, the position of spine would fluctuate during operations, which result in an increase of the difficulty in state recognition of pedicle drilling. To guarantee the safety and validity, a model-based compensation method is proposed in this paper. To build the empirical model of vertebral motion, vertebral displacement and tidal volume (Tv) signals are collected from volunteers. To rule out disturbances in original signal, FFT and wavelet transform (DWT) are used to process the experimental signals. In order to select the apt basis for different signals, the root mean square error decision-making method (RMSE-DMM) is introduced. When the filtered vertebral displacement signal is obtained, the particle swarm optimization (PSO) algorithm is used to figure out the empirical model. The robot assisted systems (RAS) can easily compensate vertebral fluctuation based on the empirical model. Due to the goal of pedicle drilling is to drill a hole from surface of first cortical layer to the interior of second cortical layer, a new key point recognition algorithm, based on force, proposed in this paper. To verify the effectiveness of compensation and the recognition algorithm, 3 sets of comparison experiments are carried out. And the results of experiment show the compensation method and new key point recognition algorithm perform effectively.