# Evaluation of a Myoelectrical Arm for Transradial Computation in Functional Activities
## 4 Discussion
This is important because, without sensation from a prosthesis, users need to rely on vision to know if they are picking items up as they cannot feel it. We used the head marker movement to represent the eye movement for all evaluation tasks. This outcome measure might be suitable for a task such as tBBT when the eyes move along with the hand. However, it might not be a good indicator for the other four tasks reflected by the data in Table 1. A new outcome measure will be identified to assess object control for these tasks. In terms of muscle activations, we did not collect the maximum voluntary contractions (MVC) for the selected muscles due to time constraints. Therefore, the reported muscle activation values were not normalized with respect to their MAVs, respectively. For the participants in the future, we will use Delsys EMG sensors to record the MVCs of the biceps, triceps, deltoid, and thoracolumbar muscles during both baseline and post-evaluation using the following procedure. The biceps MVC will be obtained by having the participant sit or stand with the forearm supported and the elbow flexed at a 90\({}^{\circ}\) angle and then instructing the participant to pull against a fixed resistance (e.g., a researcher's hand) as hard as possible, focusing on elbow flexion. The triceps MVC will be obtained by positioning the participant with the forearm supported and the elbow extended at a 90\({}^{\circ}\) angle and then instructing them to push against a fixed resistance (e.g., a researcher's hand), focusing on extending the elbow. To obtain the deltoid MVC, the participant will be positioned with the left arm abducted at a 90\({}^{\circ}\) angle from the side, and they will then be instructed to push against a fixed resistance (e.g., a researcher's hand), focusing on the shoulder abduction. To obtain the thoracolumbar muscle MVC, the participant will be instructed first to lay prone on a mat with arms extended overhead and then to lift legs and chest off the ground, focusing on contracting the middle and lower back muscles. Furthermore, the current research lacks user feedback to adequately capture the user's satisfaction with the device. Recent studies have incorporated electroencephalography (EEG) to measure a participant's satisfaction while controlling a robotic arm [38]. Other studies have found that surveys, such as the Reintegration to Normal Living Index, can accurately measure the level of satisfaction a user has regarding a prosthetic device [39]. Incorporating a means such as EEG and surveys to measure user satisfaction into this research would paint a clearer picture of the effectiveness of a training procedure and if the user has become more proficient at performing functional tasks. In summary, future researchers should consider a few key limitations identified in this research. First, the participant may have become more cognitively attuned to the flexor muscle as the muscle responsible for opening the hand, which could have led to increased muscle activation due to abnormal muscle synergy during the post-evaluation. It is also important to point out that this research utilized a small sample size; future research may need to increase the sample size in terms of task number and trial number to improve the confidence of statistical assessment of changes as more participants will be recruited. Lastly, metrics of user satisfaction should be established to quantify the effectiveness of the evaluation protocol.