Electrical and Electronic Engineering Department ROBOLAB, Sivas Cumhuriyet University, 58140, Sivas, Turkey.
Ann Biomed Eng. 2020 Apr;48(4):1218-1229. doi: 10.1007/s10439-019-02444-5. Epub 2020 Jan 2.
Breakthrough detection is a crucial task to reduce the risks of damaging soft tissue bone drilling operations during orthopedic surgery. Conventional drills are not equipped with this function while the recent literature has offered this capability with high cost and complex modification needs. In this study, a new breakthrough detection approach based on closed-loop control characteristics of the drilling operation is proposed. A feature set containing closed-loop signals and force sensor data is created to train K-Nearest and Ensemble Classifier for breakthrough detection tasks with drilling the synthetic bone model and animal bone with a robot manipulator. The best accuracy of breakthrough detection with only closed-loop control signal attributes is achieved as 96.9 ± 0.8% for the synthetic bone model and 98.1 ± 0.2% for sheep femur bone. Breakthrough detection delay which included sampling and operation time of the method guarantees that the drill bit would stop with acceptable breakthrough range of 1.0413 mm. The proposed method can be used to detect breakthrough and also to estimate the state of the drill bit in robotic orthopedic bone drilling processes using only closed-loop signals so that it would be no need to use extra high-cost sensors.
突破检测是减少矫形外科手术中软组织骨钻孔操作风险的关键任务。传统钻头没有配备此功能，而最近的文献提供了此功能，但成本高且需要复杂的修改。在这项研究中，提出了一种基于钻孔操作闭环控制特性的新突破检测方法。创建了一个包含闭环信号和力传感器数据的特征集，用于使用机器人操纵器在合成骨模型和动物骨上进行钻孔的突破检测任务训练 K-最近邻和集成分类器。仅使用闭环控制信号属性实现了最佳的突破检测精度，对于合成骨模型为 96.9 ± 0.8%，对于绵羊股骨为 98.1 ± 0.2%。突破检测延迟包括方法的采样和操作时间，可确保钻头在可接受的突破范围内（1.0413 毫米）停止。该方法可用于检测突破，也可用于在机器人矫形骨钻孔过程中仅使用闭环信号估计钻头状态，因此无需使用额外的高成本传感器。
