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Methods: An inverse finite element approach was used to compute knee kinematics from invivo measured knee forces. In vitro pilot testing indicated that the accuracy of the algorithmwas acceptable for all degrees of freedom except knee flexion angle. We therefore mounted anelectrogoniometer on a knee sleeve to monitor knee flexion while simultaneously recordingknee forces. A finite element model was constructed for each subject. The femur was flexedusing the measured knee flexion angle and brought into contact with the fixed tibial insert usingthe three-component contact force vector applied as boundary conditions to the femoralcomponent, which was free to translate in all directions. The relative femorotibial adductionabductionand axial rotation were varied using an optimization program (iSIGHT, Simulia,Providence, RI) to minimize the difference between the resultant moments output by the modeland the experimentally measured moments. Maximum absolute error was less than 1 mm inanteroposterior and mediolateral translation and was 1.2º for axial rotation and varus-valgusangulation. This accuracy is comparable to that reported for fluoroscopically measuredkinematics. We miniaturized the external hardware and developed a wearable data acquisitionsystem to monitor knee forces and kinematics outside the laboratory.Results: Knee forces were monitored in three subjects during unsupervised outdoor walking.The terrain included level ground, varying grade slopes, hiking trails, and hiking off-trail. Ingeneral knee forces were higher than those measured in the laboratory (2.2 xBW). Peak kneeforces were highest (>3 xBW) when hiking up and down a 10° slope. One subject tripped andrecorded over 5 x bodyweight.Conclusions: This method of obtaining combined kinematics and forces with minimal externalhardware greatly increases our ability for capturing true kinematics and forces. Unsupervisedactivities outside the laboratory generated significantly different forces compared to inlaboratorymeasurements. Clinically relevant data can be obtained for preclinical testing ofprostheses as well as for advising patients regarding postoperative rehabilitation and activities.We are now able to continuously monitor data over extended periods of time (days or weeks)and to record naturally occurring events (in contrast to choreographed activity). Since wecompute tibiofemoral contact as part of the algorithm to determine the kinematics, the forcesand kinematics are already integrated with contact analysis. These data can be used as inputinto damage and wear models to predict failure or for validation of biomechanical models of theknee, which predict knee forces and kinematics. Continuously monitoring in vivo knee forcesand kinematics under daily conditions will identify weaknesses and potential areas of failure incurrent designs and will provide direction into enhancing the function and durability of totalknee arthroplasty.Thursday, October 7, 2010, 15:10-15:50Session B7: Robotic Knee SurgeryIn Vivo Validated Subject-Specific Computer Model of Dynamic SquattingAfter Total Knee Arthroplastyfile:///E|/ISTA2010-Abstracts.htm[12/7/2011 3:15:47 PM]