Bone collision detection method for robot assisted fracture reduction based on force curve slope. (September 2021)

• Record Type: Journal Article
• Authors: Cai, Chenxu; Sun, Congyu; Song, Yixuan; Lv, Qinjing; Bi, Jianping; Zhang, Qinhe
• Is Part Of: Computer methods and programs in biomedicine. Volume 209(2021)
• Publication Date: 2021-09
• Subjects: Bone fracture reduction -- Viscoelastic model -- Collision detection model -- Force detection -- Slope ratio; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106315
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Bone and soft tissue have significantly different mechanical properties. When the broken bones collide, the clamping force of the broken bones changes significantly. There are many factors that affect the slope ratio of the force curve before and after the collision. The slope ratio of the force curve can be used as a benchmark for judging whether the broken bones have collided. Abstract: Background and objective: The application of robot technology in fracture reduction ensures the minimal invasiveness and accurate operation process. Most of the existing robot assisted fracture reduction systems don't have the function of bone collision detection, which is very important for system safety. In view of the deficiencies in the research of this field, a broken bone collision detection method based on the slope ratio of force curve was proposed in this paper, which could realize the real-time detection. Methods: In order to analyze the factors influencing the slope of force curve, a collision mechanical model based on three-element viscoelastic model was established. The effects of four factors on the slope ratio of the force curve were studied based on the mechanical model. The proposed collision detection model was analyzed in detail. By drawing slope ratio curves under various experimental conditions, the universality of the collision detection model was proved; by comparative simulation, the differences between the slope ratio curves before and after optimization were analyzed. The factors that affect the performance of the detection model were also analyzed. Results: The results of collision experiments show that the increase of moving speed of distal bone and soft tissue mass reduces the slope ratio, while the increase of collision angle increases the slope ratio. In the verification experiment, the minimum main peak of KRopt curve is 14.16 and the maximum is 220.7, the maximum interference value before the peak is 6.1. When the detection threshold is 10, the model can detect the collision state of the broken bone. It is also proved that after optimization, the model can effectively filter out invalid waveforms and reduce the occurrence of false detections. When a =5 and b =40, the detection model has sufficient stability and a low detection time delay. Conclusion: This research developed a broken bone collision detection method based on the slope ratio of the force curve. After optimization, the method has good adaptability under a variety of experimental conditions. The collision of broken bones can be judged by setting an appropriate detection threshold. The application of this method in the robot fracture reduction system will improve the safety of the system.