An Instrumented Spatial Linkage to Measure In-Vivo Kinematics and Motion Reproduction with a Robotic Test System
Determination of the mechanical environment of the knee joint is essential in order to understand the normal and pathological states of the tissues in that joint, mechanisms of injuries, and in developing and evaluating surgical interventions. A 6 degree of freedom Instrumented Spatial Linkage (ISL) was developed to record ovine stifle joint kinematics during normal treadmill gait, and reproduce those same motions in-vitro on a robotic testing platform in order to provide an in-vitro environment in which to examine the effects of in-vivo displacement on the soft tissues of the joint. The ISL was designed to provide increased accuracy, reduced processing time, and ease of use when compared to traditional motion capture methods. The ISL was evaluated dynamically to ensure that accurate results were maintained. The ISL and robotic test platform allow determination of the loads on the four major ligaments and the two menisci in the joint. Structural loads for 4 animals were determined for 20 representative strides beginning at hoof strike. Results showed similar loading patterns between animals but with some quantitative differences. With respect to the gait cycle beginning at hoof strike, the PCL reached a peak load just after hoof strike (5%) followed by rapid unloading. The ACL was loaded immediately thereafter with a peak at about 10% of gait. The MCL tended to follow the PCL but with a much reduced force magnitude. The LCL was largely unloaded throughout, suggesting that this ligament must play a more prominent role in other motions. For three of the four animals the average meniscal load was nearly even between the lateral and medial menisci, the fourth animal carried an average of 46.9 N more load in the lateral meniscus. Linear correlations between joint structures and kinematics were attempted but no strong correlations existed. Additionally, ligament length was also shown to be a poor predictor of ACL load. A method is proposed whereby an Artificial Neural Network can be constructed as to accurately predict ACL load given relative joint position and orientation. This study shows for the first time the loads experienced by all 4 major ligaments of the knee, and the minimum loads in the menisci in response to in-vivo motions. The load in each structure has been shown throughout gait and future studies can look at a variety of different motions and loading situations.
Engineering--Biomedical, Engineering--Mechanical
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