# Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control
## 3 Results
### Donning and Doffing Experiment
We show the trajectories for each test for the five tests for the first seven participants in [[https://youtu.be/whB_6](https://youtu.be/whB_6) Ci76 oM]([https://youtu.be/whB_6](https://youtu.be/whB_6) Ci76 oM) (participant 1), [[https://youtu.be/2](https://youtu.be/2) GKI48 cotCI]([https://youtu.be/2](https://youtu.be/2) GKI48 cotCI) (participant 2), [[https://youtu.be/iFlcD628](https://youtu.be/iFlcD628) Wnw]([https://youtu.be/iFlcD628](https://youtu.be/iFlcD628) Wnw) (participant 3), [[https://youtu.be/DzmafnQnKcQ](https://youtu.be/DzmafnQnKcQ)]([https://youtu.be/DzmafnQnKcQ](https://youtu.be/DzmafnQnKcQ)) (participant 4), [[https://youtu.be/5T5](https://youtu.be/5T5) EtZtTOWA]([https://youtu.be/5T5](https://youtu.be/5T5) EtZtTOWA) (participant 5), [[https://youtu.be/DE5](https://youtu.be/DE5) [PERSON]]([https://youtu.be/DE5](https://youtu.be/DE5) [PERSON]) (participant 6) and [[https://youtu.be/Yj2](https://youtu.be/Yj2) aICLStI0]([https://youtu.be/Yj2](https://youtu.be/Yj2) aICLStI0) (participant 7). In the videos, every test is represented by a different color: red, green, blue, cyan and magenta for test 1, 2, 3, 4 and 5, respectively. The next target for a corresponding test is displayed with the same color code at any given time. For a visual purpose, targets are plotted as circles with different diameters. Note that in the real experiments all the circles (targets) had the same size. In addition, to avoid that the plots become too cumbersome because cross crossing trajectories, only a few samples are plotted. Analyzing the videos, if lines are far away, it means that some target was missed or the path efficiency was changing from test to test. The way to check the simplicity from the user point of view is to check if the trajectories are close to the straight lines joining the previous and next target. A curve that is moving around a target means that the attempt ratio was poor (AR much greater than 1), since it means that the user was entering and leaving the target in an unstable way. This behavior of the system will be translated from the user point of view, first, as an insecure experience, and later, if he finds out that this instability is persistent in time, in a disaffection to the system. In order to show a visual interpretation of some trajectory patterns examples that determine the global PE values, Figure 7a shows the paths traveled by two users: participant #1 (red curve) and participant #5 (green curve) for targets 1 to 5. It is clear that the path between targets 2 and 3 was smoother for subject #1. The longer path traveled by subject #5 translated into a lower PE compared to subject #1 PE. There were other trajectory behaviors in this timespan that were related to the path efficiency. Note that the red trajectory was smoother not only between targets but also in the transition (when one target was achieved and the next one was shown to the user). On the contrary, the green line is slightly advanced than the red line; the user chose between a faster movement with the risk of a potential instability or at least more attention since muscle activity translates into more erratic trajectories and a smooth movement where inertia can be sometimes an annoying issue depending on the subject perception. Figure 6: PE for subject #6 (blue line) and average PE value between all participants (black line) vs. the day of experiment. This figure explains that the larger variance of the PE value for that participant is not due to instability problems of the algorithm but to his learning process (his performance improved significantly from test 1 to 5). Once the learning saturates (as we can see in the last two tests), the behavior started to be more stable like the other participants results. In Figure 7b, we show the trajectories for subjects #1 (red line) and #3 (green line) moving from target 1 to 2 and then to target 3 on test 5. The performance in this timespan gives us a visual example of the AR metric values. It is clear that subject #3 had difficulty maintaining the position in target 2.