Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China.
The Laboratory for Computational Sensing and Robotics, The Johns Hopkins University, Baltimore, Maryland, USA.
Int J Med Robot. 2021 Jun;17(3):e2233. doi: 10.1002/rcs.2233. Epub 2021 Mar 23.
Drilling is one of the most common forms of tissue removal procedures, and drilling to a desired depth contributes to avoid injury to the soft tissue beyond and ensure implant stability. The deformation of the human musculoskeletal system has been a common problem in many drilling processes, making it difficult to achieve accurate estimation of the drilling depth. To remedy this problem, a dynamic model is presented to describe the relationship between the axial vibration of the drill and the feed rate. During drilling process, the amplitude of the main harmonic is estimated from the high-frequency component of the acceleration signal, while the short-time integral of the low-frequency part is calculated. Both the initial contact of the drilling tool to the bone and breakthrough are identified by comparing either the harmonic amplitude or the short-time integral. The harmonic amplitude is mapped to the data from a non-contact position sensor tracking the feed rate of the drill. Multiple drilling experiments on both a handheld device and a robotic cutting system demonstrated the effectiveness, stability and accuracy of the method when estimating depth. The mean maximum error for drilling depth estimation is less than 15% of the simulated bone thickness when using the handheld device, while the mean maximum error is less than 5% for the robotic cutting system.
钻孔是最常见的组织切除程序之一,钻至所需深度有助于避免对软组织造成损伤,并确保植入物的稳定性。在许多钻孔过程中,人体肌肉骨骼系统的变形一直是一个常见问题,这使得难以实现对钻孔深度的准确估计。为了解决这个问题,提出了一种动态模型来描述钻头的轴向振动与进给速度之间的关系。在钻孔过程中,从加速度信号的高频分量中估计主谐波的幅度,同时计算低频部分的短时积分。通过比较谐波幅度或短时积分,来识别钻头与骨骼的初始接触和突破。将谐波幅度映射到从跟踪钻头进给速度的非接触位置传感器获得的数据。在手持设备和机器人切割系统上进行了多次钻孔实验,证明了该方法在估计深度时的有效性、稳定性和准确性。使用手持设备时,钻孔深度估计的最大平均误差小于模拟骨厚度的 15%,而对于机器人切割系统,最大平均误差小于 5%。