## 3 Knee FEA Workflow
### Pre-Processing
#### 3.1.4 Subject-Specific Motion and Loading  
Once the domain is discretized and the constitutive material models are chosen, the next step is to define the boundary conditions and loading.  
Usually, this subject-specific information is obtained via motion capture. This task can be conducted using infrared cameras and reflective markers, using inertial measurement units (IMUs), or using video recordings and AI. In addition to motion, the ground reaction forces (GRFs) are recorded with pressure sensors, and the muscular activation is captured via electromyography (EMG). Then, joint motions, moments, contact forces, and muscle forces in the knee can be estimated using inverse kinematics via musculoskeletal modeling [24; 112; 113; 114; 115]. This process is facilitated with software such as AnyBody (AnyBody Technology, Aalborg, Denmark), Vicon (Vicon Motion Systems Ltd., Oxford, UK), and OpenSim [116]. Other options to estimate these variables in the knee involve AI, where physics-based models are replaced by trained machine learning models [42; 117; 118; 119; 120]. In these models, knee loading patterns (e.g., joint forces and moments) can be predicted from measured variables (e.g., marker trajectories, GRFs, and EMGs) by organizing the input information into matrix form and optimizing a set of parameters to correlate it with the desired response. Some machine learning methods are better than others depending on the type of data and the objective of the study [117, 121].  
The implementation of mechanical loadings in knee joint models is essential for estimating the mechanical response of cartilage, menisci, ligaments, and tendons [3, 24]. However, generating subject-specific data from different physical activities can be a cumbersome task that might take weeks or months to complete. Thus, for expediting the implementation of the loading conditions, some authors have estimated them by scaling a mean gait pattern to different patients according to body weight (BW) [122], using experimental data or using musculoskeletal models [113, 114]. Another option would be using machine learning approaches with few inputs. For instance, [PERSON] et al. [123] used a mobile phone on 10 hip OA patients to estimate the hip and knee joint impulses through different exercises. Results suggested that they were able to specify the hip impulse in patients differently from a population-based average. However, the pipeline they proposed should be improved for better accuracy.  
The exact locations of tendons and ligaments in knee FE models are usually approximated, affecting the kinematics and kinetics of simulations [111, 124, 125]. This is due to the simplified geometry representation of the tissues and the variability in the insertion point locations from medical images [125, 126]. In this regard, [PERSON] et al. [1] studied knee laxity by combining experimental data and FEA. They analyzed four cadaveric knees and optimized the location of the insertion points, the pre-strain, and the stiffness of the ligaments based on one-to-one validations. The authors acknowledged the difficulties of the process and made the experimental data and the optimized model parameters freely available.