The biomechanical variation in the knee during walking that accompanies medial meniscal radial tears stemming from knee osteoarthritis (OA) has not been explored. This study introduced a finite element musculoskeletal model using concurrent lower limb musculoskeletal dynamics and knee joint finite element analysis in a single framework and expanded the models to include knees with medial meniscal radial tears and total medial meniscectomy. The radial tears involved three locations: anterior horn, midbody, and posterior horn with grades of 33%, 50%, and 83% of the meniscus width. The shear and hoop stresses of the tear meniscus and tibial cartilage contact load, accompanying tears, and postmeniscectomy were evaluated during the stance phase of the gait cycle using the models. In the 83% width midbody tear group, shear stress at the end of the tear was significantly greater than in the intact meniscus and other tear groups, and the maximum shear stress was increased by 310% compared to the intact meniscus. A medial meniscus radial tear has a much smaller effect on the tibial cartilage load (even though in the 83% width tear, the cartilage/total load ratio increased by only 9%). However, the contact force on the tibial cartilage with total postmeniscectomy was increased by 178.93% compared with a healthy intact meniscus, and the peak contact pressure after meniscectomy increased from 11.94 to 12.45 MPa to 17.64 and 13.76 MPa, at the maximum weight acceptance and push-off, respectively. Our study shows that radial tears with larger medial meniscus widths are prone to high stress concentrations at the end of the tears, leading to the potential risk of complete meniscal rupture. Furthermore, although the tears did not change the cartilage load distribution, they disrupted the circumferential stress-transmitting function of the meniscus, thus greatly increasing the likelihood of the onset of knee OA. The significant increase in the tibial cartilage load with total postmeniscectomy indicates a potential risk of OA flare-ups. This study contributes to a better understanding of meniscal tear-induced OA biomechanical changes during human activities and offers some potential directions for surgical guidance of meniscectomies and the prophylaxis and treatment of OA.

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.

Finite element musculoskeletal (FEMS) approaches using concurrent musculoskeletal and finite element 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 a major disadvantage that makes them challenging to apply in clinical environments, i.e., 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 sensors that include the tissue geometries of the entire knee joint, and that combine modeling of 16 muscles into a single framework. The model requires only the angular velocities and accelerations measured by the sensors as input. 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 (11.3 MPa) occurred on the medial side of the cartilage at the maximum loading response. The developed framework combines measurement convenience and accurate modeling, and shows promise for clinical applications aimed at understanding subject-specific biomechanics.

The half-sitting posture is essential for many functional tasks performed by industrial workers. Thus, passive lower-limb exoskeletons, known as wearable chairs, are increasingly used to relieve lower-limb loading in such scenarios. However, although these devices lighten muscle effort during half-sitting tasks, they can disrupt walking mechanics and balance. Moreover, rigorous biomechanical data on joint moments and contact forces during walking with such a device remain scarce. Therefore, this study conducted a biomechanical evaluation of level walking with a wearable chair to quantify its effects on gait and joint loading. Participants performed walking experiments with and without the wearable chair. An optical motion capture system and force plates collected kinematic and ground reaction data. Six-axis force sensors measured contact forces and moments. These measurements were fed into a Newton–Euler inverse dynamics model to estimate lower-limb joint moments and assess joint loading. The contact measurements showed that nearly all rotational load was absorbed at the thigh attachment, while the ankle attachment served mainly as a positional guide with minimal moment transfer. The inverse dynamics analysis revealed that the wearable chair introduced unintended rotational stresses at lower-limb joints, potentially elevating musculoskeletal risk. This detailed biomechanical evidence underpins targeted design refinements to redistribute loads and better protect lower-limb joints.

Background/Objectives: In recent years, 3D shape models of the human body have been used for various purposes. In principle, CT and MRI tomographic images are necessary to create such models. However, CT imaging and MRI generally impose heavy physical and financial burdens on the person being imaged, the model creator, and the hospital where the imaging facility is located. To reduce these burdens, the purpose of this study was to propose a method of creating individually adapted models by using simple X-ray images, which provide relatively little information and can therefore be easily acquired, and by transforming an existing base model. Methods: From medical images, anatomical feature values and scanning feature values that use the points that compose the contour line that can represent the shape of the femoral knee joint area were acquired, and deformed by free-form deformation. Free-form deformations were automatically performed to match the feature values using optimization calculations based on the confidence region method. The accuracy of the deformed model was evaluated by the distance between surfaces of the deformed model and the node points of the reference model. Results: Deformation and evaluation were performed for 13 cases, with a mean error of 1.54 mm and a maximum error of 12.88 mm. In addition, the deformation using scanning feature points was more accurate than the deformation using anatomical feature points. Conclusions: This method is useful because it requires only the acquisition of feature points from two medical images to create the model, and overall average accuracy is considered acceptable for applications in biomechanical modeling and motion analysis.