# Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control
## 4 Discussion  
An important handicap of the current myoelectric prostheses controls is the performance degradation suffered facing daily EMG pattern alterations. These effects emerge as a result of \"noisy\" factors that alter the input data. An ideal control should overcome these disturbances and last for a long time under different real-life scenarios before the performance starts to degrade and a re-calibration is necessary. In this manuscript, we have analyzed whether the adaptive IIR linear filtering control previously described [49] satisfies these robustness requirements for an optimal prostheses control. The results demonstrate that the performance of this recently proposed method was not affected by the time elapsed since training or the small electrode shifting deviations due to donning/doffing the armband in consecutive days that alter the EMG patterns. Moreover, we also show that the models trained in a specific arm position kept the high performance in other arm positions and that this position can be changed at any time. Thus, the important novelty of this work relies on the capabilities of the algorithm to stay stable through different disturbances without any re-adjustment. In spite of the encouraging results, a larger number of subjects must be tested in order to enhance the statistical confidence in the results reported in this paper. In addition, we have to experiment with limb deficiency subjects in order to analyze if the robustness is maintained in real patients. It has to be noted that in this study we used wrist movements to control the active DoFs. This is not a restrictive choice. As far as there are enough independent patterns, any muscle activation could be used as input even if the EMG patterns do not perform a natural movement as it could happen in participants with limb deficiencies. For some patients, such as amputees, the limitation in the EMG signals that they can generate will limit the number of DoFs and the pattern selection. At the end, EMG patterns will be mapped to the available movements in the prosthesis no matter which is the biological origin. If desired, the EMG patterns could be selected to match the prosthesis functions. In summary, the algorithm performance relies only on the independence of the EMG signal patterns and not on the biological movement related to them. Of course, the larger the signal to noise ratio, the better the system will be able to map the EMG signals into the desired movements. In order to exploit the benefits of the algorithm, the training protocol was implemented as a co-adaptive task in real-time. Due to the IIR adaptive algorithm characteristics (the previous estimated output is used to estimate the next output in addition to the new EMG signals), the system was able to learn the dynamics of the user-computer interaction in real time, avoiding any post processing and allowing to obtain a velocity controller in a natural way. If one of the agents does not operate properly, the other part will become confused, affecting its learning and performance. Figure 9: Trajectories during free arm position test. During this test, the participants were asked to change the arm position every three targets. Each color represents the estimations for the period of time (three targets) that one arm position was held. Three sets of three targets and the trajectories that followed between them are plotted: Set A (green), Set B (blue) and Set C (red). Starting from A1, after reaching the last target of each set, A3 and B3 (which means changing the arm position), the plot shows how the trajectories became more erratic. Once the user adapted to the new position, the behavior returned to a smooth control. In practice, it means that the user must try to generate similar EMG patterns for a given movement. This is the main advantage of using an adaptive filtering approach: the learning of a continuous and dynamic environment enhances the capability to overcome small variances in the input data.