State detection of bone milling with multi-sensor information fusion

To address the safety issues of bone drilling, especially bone screw path drilling, this paper proposes a new method to detect the bone drilling state. The proposed method performs pattern recognition based on the results of multi-sensor information fusion. A support vector machine is selected as the pattern classifier, and the adopted signals include the force, current, feed speed, rotation speed and deflection of the robotic arm. Four different drilling states, i.e., the cortical, cortical-transit-cancellous, almost-break-cortical and cancellous states, are detected, and then help the surgical robot system to achieve safe bone drilling. The proposed method is validated and analyzed through an experiments on pig scapula, and found to have potential clinical application to the bone drilling process in vertebral, leg, ear bone, mandible, and other related orthopedic surgeries.