Search Results (3)

Background: Anterior knee pain is a prevalent issue post total knee arthroplasty, often necessitating revision surgery. Various factors contribute to this complication, including patellar maltracking and excessive patellofemoral load. Kinematic alignment has emerged as an alternative, showing promising outcomes in clinical studies. However, its impact on patellofemoral biomechanics needs to be more adequately understood. This study compared the effects of kinematically versus mechanically aligned total knee arthroplasty on patellofemoral joint biomechanics. Methods: Eight fresh-frozen human knee specimens underwent biomechanical testing in a knee rig setup, performing an active weight-loaded knee joint flexion of 30–130°. After the testing of native kinematics, kinematically and mechanically aligned total knee arthroplasty was performed using a medial pivot implant design without patellar resurfacing. Quadriceps force, retropatellar peak pressure and the retropatellar contact area were measured during knee flexion using a patellar pressure-sensitive film. Patella kinematics (shift and tilt) was tracked using an optoelectrical measurement system. Functional regressions were used to determine the influence of the alignment on the kinematics and loading of the knee joint. Results: Kinematically aligned total knee arthroplasty resulted in reduced quadriceps force during knee flexion compared to mechanically aligned total knee arthroplasty. Retropatellar peak pressure, retropatellar contact area and patella kinematics did not vary between the alignments. Conclusions: Kinematic alignment offers potential benefits in reducing quadriceps force during knee flexion, which may mitigate anterior knee pain risk. Further research is needed to elucidate its effects in varying anatomical conditions and alignment strategies. Full article

In order to evaluate the tracking capability of optoelectric tracking for an orbital target, the limit magnitude detection performance calculation model and its calculation method are studied. Combining the optical signal characteristics of the tracked orbital target, the background, and the CCD noise, the framework of the limit magnitude calculation model of the system for dynamic target detection is constructed. The relationships between the limit magnitude and the signal-to-noise ratio threshold of the optical signal characteristics, the exposure time of the CCD camera, and the dark current of the CCD imaging are studied and analyzed while considering the sunlight illumination condition, so that the calculation function and its change curve are given. The limit magnitude detection capability of the system is verified by the simulated experiment and the synchronized tracking test, and the detection distance maximum error of the model calculation is 3.6 m. The results show that under certain illumination conditions, when the exposure time of the CCD camera is longer and the SNR threshold is lower, the limit magnitude detection performance of the system is better, and the tracking performance of the system is more stable. Full article

As an important angle sensor of the opto-electric platform, gyro output accuracy plays a vital role in the stabilization and track accuracy of the whole system. It is known that the generally used fixed-bandwidth filters, single neural network models, or linear models cannot compensate for gyro error well, and so they cannot meet engineering needs satisfactorily. In this paper, a novel hybrid ARIMA-Elman model is proposed. For the reason that it can fully combine the strong linear approximation capability of the ARIMA model and the superior nonlinear compensation capability of a neural network, the proposed model is suitable for handling gyro error, especially for its non-stationary random component. Then, to solve the problem that the parameters of ARIMA model and the initial weights of the Elman neural network are difficult to determine, a differential algorithm is initially utilized for parameter selection. Compared with other commonly used optimization algorithms (e.g., the traditional least-squares identification method and the genetic algorithm method), the intelligence differential algorithm can overcome the shortcomings of premature convergence and has higher optimization speed and accuracy. In addition, the drift error is obtained based on the technique of lift-wavelet separation and reconstruction, and, in order to weaken the randomness of the data sequence, an ashing operation and Jarque-Bear test have been added to the handle process. In this study, actual gyro data is collected and the experimental results show that the proposed method has higher compensation accuracy and faster network convergence, when compared with other commonly used error-compensation methods. Finally, the hybrid method is used to compensate for gyro error collected in other states. The test results illustrate that the proposed algorithm can effectively improve error compensation accuracy, and has good generalization performance. Full article