# Evaluation of a Myoelectrical Arm for Transradial Computation in Functional Activities
## 2 Methodology
### Evaluation Protocol  
The performance evaluation consists of two visits to the Center of Biomechanics and Rehabilitation Engineering (CBRE) at UDC and the training program at home for two weeks. The first and second visits were used to evaluate the baseline and post-training performances of the user with i-Limb Access, respectively. Each visit took roughly two hours. The evaluation protocol implemented an experimental setup, as shown in Figure 1, utilizing the Vicon motion capture system (Vicon Motion Systems, Oxford, UK), Tekscan Strideway pressure mat (Tekscan, Inc., Norwood, MA, USA), and wireless EMG modules (Delsys, Natick, MA, USA) to identify the outcome measures. The sampling rates for these systems are 100 Hz, 40 Hz, and 1000 Hz, respectively. The participant stood in front of a testing platform made of a lab stool whose height was adjusted by placing the non-metal boards on the top to ensure that the participant could comfortably perform the experimental tasks. The data was collected and analyzed via a pipeline that calculates joint kinematics, muscle activities, and the location of the center of pressure from preprocessed marker data, EMG data, and vertical ground reaction force (vGRF) data. At the beginning of each visit, 41 reflective markers were placed on the participant's body. In detail, 39 14-mm markers were placed at bony anatomical landmarks of the full body in accordance with the Vicon Full Body Plug-In-Gait body model [25]. Two additional 14-mm markers were placed at the medial side of the elbow joints. EMG electrodes were placed on the muscle belly region of the middle deltoids, biceps, and triceps muscle in the left arm, and thoracolumbar fascia muscle in the lower back. As there is very limited access to forearm muscles, these muscle groups are selected to evaluate the improvement in upper limb muscle synergy and trunk compensation reduction when performing the tasks. It is expected that the EMG activations recorded from these muscles will decrease as the participant learns to be adapted to the device to achieve the desired movement. Monitoring the activation of these muscles helps assess indirectly how well the user was able to generate and control the signals needed for precise prosthetic control while also ensuring proper alignment and stability during movement. Preprocessing the motion capture and EMG data was performed by using Vicon Nexus. The vGRF data were preprocessed using Strideway software (7.80 x64). The Strideway and Nexus software (2.12.1) was integrated using a USB 6008 data acquisition card, enabling the systems to start and stop data collection at the same time. For the evaluation tasks, it is necessary to have a combination of unimanual and bimanual tasks that are similar to the training exercises to determine if the user has become more effective in using the device as opposed to simply more effective at performing the training exercises. There are two bimanual (box lift and bottle cap) and two unimanual (functional) (target Box and Block Test (tBBT) [26], bottle pour, and pulley) tasks. The box lift task consists of the participant bending down to pick up a box from the ground and placing it on the table. The bottle cap task requires the participant to pick up a bottle with a screw-on cap with an unimpaired hand and then use the prosthetic device to unscrew the cap. The tBBT task consists of moving colored blocks from one side of a box across a partition to the other side. The bottle pour task involves lifting a bottle and pouring its contents out into a cup. The pulley task involves reaching up to grab a ring handle hanging from a pulley system. The participant then pulls the handles down until they are at waist level, then lets the handles rise back to the starting position. Figure 1: The experimental setup in which a participant performed a bottle pour task with i-Limb Access. This participant performed two sessions for each task. Each session was suggested to be completed in less than 40 s if possible.