This study aims to investigate the impact of tibial tray fixation peg structure in posterior stabilized (PS) knee prostheses on its initial fixation stability, a finite element model and a micromotion prediction model of PS total knee arthroplasty (TKA) were established to comparatively study the differences in the von Mises stress of the proximal tibia and the micromotion at the bone-prosthesis fixation interface under four PS tibial tray fixation peg design, namely cylindrical plus hemispherical, cylindrical plus conical, hexagonal prism, and cruciform. The results showed that, at the moment of the maximum force of knee joint during level walking activity, there was no significant difference in the tibial von Mises stress between the tibial tray with or without fixation peg designs. However, the peak micromotions at the prosthesis fixation interface of all tibial trays with fixation peg design were significantly reduced. Among them, the micromotion suppression effect of the cruciform fixation peg was the most obvious. At the moment of the maximum flexion angle of knee joint during squatting activity, the tibial von Mises stress for tibial trays with fixation peg design was clearly lower than that without fixation peg design, meanwhile the peak micromotion at the prosthesis fixation interface was also significantly reduced. Overall, the cruciform fixation peg design showed the best fixation stability and effectively reduced the loosening risk at the prosthesis fixation interface. This study recommended that the backside of the tibial tray in non-cemented PS knee prostheses adopted a design combining a cylindrical stem with a serrated keel and a cruciform fixation peg. This study provided an important reference basis for improving the initial fixation stability of non-cemented PS knee prostheses by optimizing the backside design of the tibial tray.
Traditional gait analysis systems are typically complex to operate, lack portability, and involve high equipment costs. This study aims to establish a musculoskeletal dynamics calculation process driven by Azure Kinect. Building upon the full-body model of the Anybody musculoskeletal simulation software and incorporating a foot-ground contact model, the study utilized Azure Kinect-driven skeletal data from depth videos of 10 participants. The in-depth videos were prepossessed to extract keypoint of the participants, which were then adopted as inputs for the musculoskeletal model to compute lower limb joint angles, joint contact forces, and ground reaction forces. To validate the Azure Kinect computational model, the calculated results were compared with kinematic and kinetic data obtained using the traditional Vicon system. The forces in the lower limb joints and the ground reaction forces were normalized by dividing them by the body weight. The lower limb joint angle curves showed a strong correlation with Vicon results (mean ρ values: 0.78 ~ 0.92) but with root mean square errors as high as 5.66°. For lower limb joint force prediction, the model exhibited root mean square errors ranging from 0.44 to 0.68, while ground reaction force root mean square errors ranged from 0.01 to 0.09. The established musculoskeletal dynamics model based on Azure Kinect shows good prediction capabilities for lower limb joint forces and vertical ground reaction forces, but some errors remain in predicting lower limb joint angles.