Tactile Perception for Surgical Status Recognition in Robot-Assisted Laminectomy

Abstract

In laminectomy, the laminas above the spinal nerves need to be removed by milling. This process is very dangerous because of the special lesion, and surgeons usually control the milling process by sensing the surgical status. However, in robot-assisted laminectomy, safety monitoring will be difficult because the surgeon is not directly involved. Based on the human tactile perception mechanism, this article proposes a surgical status perception method using the acceleration sensor and force sensor installed on the robot. The Sinc-convolutional layer is introduced to process high-frequency vibration signals, and the obtained features are processed together with the averaged force signals using a 1-D convolutional network. The proposed method classifies the surgical status into five categories, and outputs the burr clogging probability. Experiments on animal bones show the effectiveness of the proposed model. In addition, it has also been proved that the fusion of the two tactile signals can significantly improve the status recognition accuracy under changing milling parameters.