A Computationally Efficient Lower Limb Finite Element Musculoskeletal Framework Directly Driven Solely by Inertial Measurement Unit Sensors

Abstract

Abstract Finite element musculoskeletal (FEMS) approaches using concurrent musculoskeletal (MS) and finite element (FE) models driven by motion data such as marker-based motion trajectory can provide insight into the interactions between the knee joint secondary kinematics, contact mechanics, and muscle forces in subject-specific biomechanical investigations. However, these data-driven FEMS systems have two major disadvantages that make them challenging to apply in clinical environments: they are computationally expensive and they require expensive and inconvenient equipment for data acquisition. In this study, we developed an FEMS model of the lower limb, driven solely by inertial measurement unit (IMU) sensors, that includes the tissue geometries of the intact knee joint and combines muscle modeling and elastic foundation (EF) theory-based contact analysis of a knee into a single framework. The model requires only the angular velocities and accelerations measured by the sensors as input, and the target outputs (knee contact mechanics, secondary kinematics, and muscle forces) are predicted from the convergence results of iterative calculations of muscle force optimization and knee contact mechanics. To evaluate its accuracy, the model was compared with in vivo experimental data during gait. The maximum contact pressure (12.6 MPa) in the rigid body contact analysis occurred on the medial side of the cartilage at the maximum loading response. The proposed computationally efficient framework drastically reduced the computational time (97.5% reduction) in comparison with the conventional deformable FE analysis. The developed framework combines measurement convenience and computational efficiency and shows promise for clinical applications aimed at understanding subject-specific interactions between the knee joint secondary kinematics, contact mechanics, and muscle forces.