At present, computational modeling has not been utilized as a design tool for total knee replacement (TKR). Also, classifying a new design as successful usually requires many years of long-term clinical follow-up studies. Computational modeling presents an opportunity to contribute to implant design evaluations and prediction of long-term success, during the early stages of the implant design process. The purpose of this study was to construct a computational model that will determine and compare in vivo dynamic forces and torques of the non implanted and implanted knees. It is hypothesized that this model will provide valuable information pertaining to post-implantation boundary conditions during the design phase.

A three-dimensional (3-D), inverse dynamics model of the human lower limb was created. System differential equations were derived for the human lower extremity using Kane’s theory of dynamics.Input kinematics were obtained for five normal knees and five posterior stabilized TKR, determined while subjects performed deep knee bend while under fluoroscopic surveillance. Musculo tendinous units were assumed to act along straight line segments, and ligamentous units were represented by nonlinear elastic elements. Knee kinetics were calculated and compared fo reach group and a comparison was conducted.

Kinetics were much more variable for the TKR group, and tibiofemoral contact forces were on average higher than the normal group: 2.47 times body weight (BW) and 2.21 BW, respectively. Increased posterior femoral rollback lead to lower axial contact forces and lower quadriceps forces in both groups. Force patterns were very sensitive to input patient specific kinematics.

The predicted tibio femoral forces were higher in TKR subjects, which is consistent with current clinical knowledge. Force patterns for the normal subjects were more consistent than those forthe TKR subjects, which was primarily attributed to the greater variance in kinematics for the TKR subjects. This study represents a first step in constructing a design facilitation tool for TKR technology. Successful designs will be determined by producing kinetic patterns most similar to normal knee patterns.