## 7 Discussion  
Looking at the pairwise comparison for the performance metrics from study A (Table 2), there appears to be a trend of the scenarios with multiple objects lacking significance and vice versa. For the multi-object scenarios, there is a lack of statistical significance in 11 out of 18 cases, whereas it is 4 out of 18 cases for the single object scenarios. Another interesting observation in relation to this is how the different control schemes behave when moving from a scenario with only a single object to one with multiple objects. In the case of the non-autonomous control scheme, the introduction of multiple objects does not seem to alter any of the measured performance metrics much. The opposite is true for both the adaptive and fixed semi-autonomous control, as most of their metrics in Figure 8 appear to increase when introducing multiple objects. This is expected, as having multiple objects in the scene requires the system to predict the intention of the user. However, these results indicate that improving the current approach for intention prediction could be beneficial. The current method for intention prediction can be considered amnesic as it relies solely on the current state, i.e., where the object is in relation to the hand right now. It could be beneficial to use prior information in a memory-based approach for the intent prediction, such as considering the entire trajectory traveled by the hand so far [25; 26]. The idea of having two different objects in the two studies was to provide varying levels of difficulty. Looking at the results from both studies A and B, in Figure 10, it appears that this choice was successful as some scenarios are clearly more difficult than others when using the manual control scheme. For example, the strawberry generally takes a longer time and requires more commands to pick up as compared to the bottle. This is not surprising as the small size of the strawberry often requires a pincer grasp where the wrist of the exoskeleton needs to be rotated. The bottle can instead be grasped using a palm grasp where there is no need to rotate the wrist of the exoskeleton. In further studies, it may hence be beneficial to include even more of these difficult tasks as the benefits of using semi-autonomous control are more pronounced in these cases. This is also clear when looking at the pairwise comparisons for both study A (Table 2) and study B (Table 4), where the scenarios involving a strawberry account for the majority of the statistically significant  
Figure 11: Study B—Box plots of the scores for the different control schemes for the two questionnaires; (a) INTUI measuring intuitiveness (higher is better) and (b) NASA-TLX measuring the task load (lower is better). results. This observation of semi-autonomous control being more beneficial for difficult tasks has been made in several other studies as well [18; 20]. However, this difference in performance between grasping the strawberry and the bottle is less apparent for the adaptive and fixed semi-autonomous controls. A possible explanation is the fact that both these two control schemes can simultaneously adjust both the position and orientation of the exoskeleton. Performing simultaneous control of both the position and orientation is not possible for the non-autonomous control as it would require issuing two commands at once, which is not possible due to the nature of the tongue-based interface. The simultaneous adjustment of both position and orientation is hence a clear benefit of using either the adaptive or fixed semi-autonomous control instead of the non-autonomous, i.e., manual, control. In the results for the performance metrics from study B, there is no significant difference between the adaptive semi-autonomous control and the fixed version. This differs from the results found in study A, where the adaptive semi-autonomous control resulted in a significant reduction in at least a few cases, especially when considering the number of commands used.