# Evaluation of a Myoelectrical Arm for Transradial Computation in Functional Activities
## 2 Methodology
### Data Analysis  
Our data analysis includes three components: data preprocessing, data storage, and data postprocessing. Preprocessing the motion capture data in Vicon Nexus began with reconstructing the markers in the visualizer and auto-initializing labeling using the full-body plug-in gait model. The participant was then scaled by applying Vicon labeling skeleton measurements, followed by a static skeleton calibration where marker locations related to joints were saved. The data were cropped into the region of interest, and any unlabeled or mislabeled markers were manually corrected. Missing data were filled using one of Nexus' four gap-fill strategies, and the trajectory was smoothed using the Woltring filter. The static plug-in gait model was applied to obtain and verify biomechanical  
Figure 2: Joint angle demonstration. (**A**) Trunk tilting—a positive value corresponds to forward trunk tilt. Here, the trunk is tilting at +15\({}^{\circ}\). (**B**) Trunk listing—a positive value corresponds to leaning to the right side. Here, the trunk is listing at +15\({}^{\circ}\). (**C**) Trunk rotation—a positive value corresponds to rotating toward the right side. Here, the trunk rotates +15\({}^{\circ}\). (**D**) Shoulder flexion—a positive value corresponds to the situation in which the arm is in front of the body. Here, the left shoulder has a flexion angle of +60\({}^{\circ}\). (**E**) Shoulder abduction—a negative value corresponds to an outwardly moved left arm. Here, the left shoulder has an abducted angle of \(-60^{\circ}\). (**F**) Shoulder rotation—a positive value corresponds to an internally rotated humerus of the left arm. Here, the left shoulder has a rotation angle of +60\({}^{\circ}\). As a result, the left palm faces into the body.  
outputs, such as joint angles, in the calibration trial before proceeding to the dynamic trial. The dynamic trial followed similar steps, applying the dynamic plug-in gait model and exporting the marker and joint angle data in a.csv format. A wireless Delsys EMG system was used to record muscle activation of the biceps, triceps, and mid-deltoid from the left arm and from at 1000 Hz. EMG data were band-pass filtered at 15-380 Hz, full-wave rectified, and low-pass filtered at 7 Hz to create a linear envelope using a custom script [25,36] developed in MATLAB R2023b (MathWorks, Natick, MA, USA).  
A data storage plan was created using a MATLAB-based structure to store processed data (marker data, joint angle data, EMG data, vGRF, and COP data) collected from Vicon Nexus integrated with Delsys wireless EMG and Strideway platform, and the participant's anthropometric data. Time normalization was then used to compare the outcome measures between baseline and post. A data postprocessing pipeline was also developed in MATLAB to achieve batch processing for these outcome measures in all experimental conditions and by implementing statistical tools to compare the joint kinematics (including trunk and shoulder rotation angles in three axes), smoothness of movement, muscle activities, and center of pressure before and after completion of the training program.