## 2 Materials and Methods
### Data Collection and Processing  
Kinematic and ground reaction force data were identified and acquired using Vicon Nexus 2.14.0 software [8; 16]. Data processing, including coordinate system conversions, low-pass filtering, extraction, and format conversions, was performed using MATLAB version 2023b (The MathWorks, Natick, MA, USA) [10; 29]. Finite element analysis was conducted using OpenSim software (Stanford University, Stanford, CA, USA) to calculate biomechanical parameters as boundary conditions. Surface electromyographic (EMG) data were recorded using a Delsys EMG system (Delsys, Boston, MA, USA) at a frequency of 1000 Hz. The system was used to monitor muscle activation in the following muscles: semitendinos, soleus, rectus femoris, medial gastrocnemius, vastus medialis, vastus lateralis, biceps femoris, and tibialis anterior. The placement of the EMG electrodes was performed by trained and certified professionals with expertise in EMG techniques, ensuring proper electrode positioning for accurate signal acquisition. The EMG signals were first preprocessed using standard procedures, including filtering and rectification, before further analysis. These preprocessing steps helped eliminate noise and artifacts, ensuring the reliability of the data. To ensure high-quality signal acquisition, the skin at the electrode placement sites was carefully prepared by removing hair, lightly abrading the skin, and cleaning with alcohol to reduce impedance at the skin-electrode interface. The placement of the electrodes was conducted by trained and certified professionals with expertise in EMG techniques, ensuring proper positioning based on SENIAM guidelines (Figure 1B) [30]. Additionally, a certified neurologist with extensive experience in performing and analyzing EMG data supervised the process to ensure the accuracy and reliability of the data collection. For each participant, maximal voluntary contraction (MVC) trials were recorded for all monitored muscles after electrode placement. The participants then stood on a force plate to collect static coordinates used for musculoskeletal modeling. During the trials, the EMG system was calibrated and monitored continuously by a trained specialist to ensure optimal performance. The raw EMG signals underwent a series of preprocessing steps to ensure data reliability and accuracy. A fourth-order bandpass filter (10-500 Hz) was applied to remove noise and artifacts, followed by full-wave rectification of the signals. Subsequently, a 10 Hz low-pass filter was used to generate smoothed EMG envelopes. The root mean square (RMS) of the filtered signals was then calculated for each muscle, and the RMS amplitude was normalized to the maximum RMS amplitude recorded during maximal voluntary contraction (MVC). This normalization process ensured the comparability of muscle activation levels across participants and muscles. The EMG data were integrated into a musculoskeletal model created in OpenSim 4.4. Each participant's anthropometric model was scaled using the OpenSim scaling tool to account for individual variations in limb length and body size [6; 27]. The process began with importing a static model into OpenSim, followed by scaling the muscle origins, insertions, and force arms based on the participant's measurements. Marker data from the trials and external force files were then processed using the Inverse Kinematics (IK) tool to compute joint angles and motions [27]. To ensure consistency with the measured dynamic data, the Residual Reduction Algorithm (RRA) was applied to smooth the kinematic data. Finally, the smoothed kinematic data were used in Computational Muscle Control (CMC) to calculate muscle activation and force outputs (Figure 2) [31]. Figure 3 illustrates the comparison of EMG-based muscle activation data with the simulated muscle activation values generated by the musculoskeletal model for each muscle. These analyses confirmed the reliability of the EMG data in validating muscle activation and force simulations.