# Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control
## 4 Discussion
A first approach focused on the effect of small electrode shifting and the time that alters the EMG patterns. The donning/doffing experiment proved that the models learned in a short training (128 s) are usable for at least four days, keeping a high performance. Neither the removal of the sensor nor the long-term use affected the system behavior. In some ongoing tests, some patients have been using the same model for months without significant degradation. These observations strongly support that the model is robust enough to overcome these two real life disturbances. Previously, the FIR version of the model [51] was tested against similar disturbances in [39] being significantly affected by the donning/doffing of the electrodes. Overcoming these disturbances is another benefit of the novel IIR version. Another important aspect of the study is the analysis of the influence of arm position. Modifying the arm posture introduces other disturbance sources as gravity, fatigue or EMG offsets that alter the initial EMG patterns. The results proved that the model was able to generalize from single arm positions to unknown situations keeping a high and stable performance. This is essential for a natural prosthesis control where the user has to be able to control the hand movement with similar EMG patterns no matter the position of the arm. Other interesting observations came out from this experiment. Note that P1 position gave better results regardless of the training position used to learn the controller. Initially, one could expect that the best results during the test would be achieved by the same position used while training. One possible reason for these results is the effect that external conditions as fatigue or other muscle activity can generate in the system performance. In P1 position the arm is completely relaxed except for the active muscles used to perform the desired movement. Other positions (P2 and P3) require strength to hold the arm position, involving phenomena like gravity, fatigue or tension. These factors will generate and offset in the EMG patterns, non-related to the control task. The absence of this needless information and effort in P1 would be evidenced in a slight improvement on the performance metrics. Our findings also show that the algorithm is robust and efficient even in dynamic situations as the test P4, where participants reproduced a more realistic use. The change of the arm position every three targets generated a transient period where control became erratic since the signal to noise ratio became very low. However, this was quickly overcome by user adaptation, recovering the control and the high performance in a short time. On the other hand, none of the tested training positions seemed to be significantly better for training the model as we could have expected. This makes us think that the clue is again the consistency of the EMG patterns. As far as this is accomplished, this algorithm will learn a high performance model. This is possible thanks to the natural way in which the system learns that a velocity control is the best strategy. Note that in a system where the learning procedure is based on position control and during post-processing the velocity control is enforced, the system lacks the ability to respond in real time to changes in the input signal patterns. The observations proved that the model was robust enough against the EMG variations generated by different arm positions. This is strictly necessary for a natural prosthesis control where the users arm movements should not affect the prosthesis behavior. In summary the new model has a high and robust performance in all the real life conditions presented for our able-bodied participants. After facing different types of disturbances that alter the EMG signals, we found out a general benefit of the new model applied to all of them.