Bone collision detection method for robot assisted fracture reduction based on vibration excitation.

Computer methods and programs in biomedicine(2022)

引用 0|浏览2
暂无评分
摘要
BACKGROUND AND OBJECTIVE:In the process of robotic fracture reduction, there is a risk of unintended collision of broken bones, which is not conducive to ensuring the safety of the reduction system. In order to solve this problem, this paper proposed a vibration-based collision detection method for fracture reduction process. METHODS:Based on the two degree-of-freedom vibration response model, the factors affecting the respond of the vibration, including the excitation voltage, the clamping length at the proximal and distal ends, the mass and tensile force of the soft tissue, were obtained. The effects of these factors on the vibration transfer performance of broken bones and soft tissue were investigated by single factor experiments. RESULTS:The results showed that, in terms of peak value, the increase of excitation voltage would make the vibration amplitude increase linearly, and the increase of soft tissue mass and tension increased the vibration transmission capacity of soft tissue in the frequency range of 500-1000 Hz. In terms of peak frequency, the clamping length at the distal end had the greatest influence, which reached 74 Hz, followed by 45 Hz at the proximal end. While the influence of other factors was little. According to single factor experiments, the excitation frequency in the verification experiments was determined as 677 Hz. Under the vibration interference with the acceleration amplitude of 1.2 G, this method achieved correct detection. CONCLUSION:This research developed a broken bone collision detection method based on vibration excitation. The method can correctly detect the collision of broken bones with strong anti-interference ability. It is of great significance to improve the safety of fracture reduction process.
更多
查看译文
关键词
Fracture reduction,Vibration response,Excitation frequency,Collision detection,Surgery safety
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要