Development of a 3D-knee brace joint using MRI data and a genetic algorithm

Abstract Approximately 40% of sports-related injuries affect the knee joint, with the anterior cruciate ligament particularly susceptible to injury. The use of knee orthoses for the prevention of knee injuries has become a common practice in sports such as ice hockey, American football, and alpine skiing. However, orthoses cannot accurately reproduce the complex kinematics of the knee. This might be associated with a decrease in performance and comfort. This study aims to improve the congruence between orthotic joints and a measured knee motion. Knee kinematics of four male participants were recorded using a combination of video motion capture and MRI. Finite helical axes were computed to design the new orthotic joint. The new joint consists of two four-bar linkages optimized by a genetic algorithm to approximate the finite helical axis positions. A multi-body simulation was used to determine the change of constraining forces in the knee using the new orthosis compared to a conventional one. The mean squared error between knee and orthosis motions for the medial joint parts was 0.07 to 2.04 mm and 1.1 to 10.65 mm for the lateral joints. The total MSE was 3.46 mm. The simulation results showed substantially reduced constraining forces for the new joint, indicating a better match with natural knee kinematics. This improvement could increases orthosis effectiveness and comfort, thereby accelerating the healing process. In conclusion, the introduced approach allows a more accurate replication of the knee’s natural movement, thereby minimizing constraining forces in the knee and possibly improving comfort.