Abstract
Background
While in vivo kinematics and forces in the knee have been studied extensively, these are typically measured during controlled activities conducted in an artificial laboratory environment and often do not reflect the natural day-to-day activities of typical patients. We have developed a novel algorithm that together with our electronic tibial component provide unsupervised simultaneous dynamic 3-D kinematics and forces in patients.
Methods
An inverse finite element approach was used to compute knee kinematics from in vivo measured knee forces. In vitro pilot testing indicated that the accuracy of the algorithm was acceptable for all degrees of freedom except knee flexion angle. We therefore mounted an electrogoniometer on a knee sleeve to monitor knee flexion while simultaneously recording knee forces. A finite element model was constructed for each subject. The femur was flexed using the measured knee flexion angle and brought into contact with the fixed tibial insert using the three-component contact force vector applied as boundary conditions to the femoral component, which was free to translate in all directions. The relative femorotibial adduction-abduction and axial rotation were varied using an optimization program (iSIGHT, Simulia, Providence, RI) to minimize the difference between the resultant moments output by the model and the experimentally measured moments. Maximum absolute error was less than 1 mm in anteroposterior and mediolateral translation and was 1.2° for axial rotation and varus-valgus angulation. This accuracy is comparable to that reported for fluoroscopically measured kinematics. We miniaturized the external hardware and developed a wearable data acquisition system 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. In general knee forces were higher than those measured in the laboratory (2.2 xBW). Peak knee forces were highest (>3 xBW) when hiking up and down a 10° slope. One subject tripped and recorded over 5 x bodyweight.
Conclusions
This method of obtaining combined kinematics and forces with minimal external hardware greatly increases our ability for capturing true kinematics and forces. Unsupervised activities outside the laboratory generated significantly different forces compared to in-laboratory measurements. Clinically relevant data can be obtained for preclinical testing of prostheses 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 we compute tibiofemoral contact as part of the algorithm to determine the kinematics, the forces and kinematics are already integrated with contact analysis. These data can be used as input into damage and wear models to predict failure or for validation of biomechanical models of the knee, which predict knee forces and kinematics. Continuously monitoring in vivo knee forces and kinematics under daily conditions will identify weaknesses and potential areas of failure in current designs and will provide direction into enhancing the function and durability of total knee arthroplasty.
