## 7 Discussion
Another major difference between the results of study A and B is a consistent decrease in performance for all the different control schemes. In general, the participants in study B use longer time, more commands, and the hand of the exoskeleton travels further in comparison to the participants from study A. This is despite the task being identical and using the exact same system in terms of both hardware and software. The results from study B also appear to carry less statistical power than the results found from study A; this is likely due to the smaller sample size, i.e., number of participants, but also partly due to a higher amount of noise in the collected measurements for study B. The presence of more noise is clear when looking at the distribution of measurements in study A and B in Figure 10.  
A likely explanation for the difference in the results could be the different structure used in the two studies. In study B, the participants had less time for training to use the system, both in terms of using the ITCI, learning how the exoskeleton moves, and how the different control schemes behave. This could have impacted the performance of the participants in study B as the ITCI may have a relatively long learning curve [38], even though most learning takes place within the first 3 days. Another possible factor contributing to the different performance between studies A and B could the age difference. The mean age of the participants in study B is over twice the age of the participants in study A (25 versus 55 years). Similar observations were made in a study on controlling a computer using neck movements, where performance decreased as the age of the participants increased [39]. This could indicate that age is indeed a factor when using the proposed system. Looking at the NASA-TLX and INTUI questionnaires, they confirm many of the same observations made from the performance metrics for both studies. For study A, both the adaptive and fixed semi-autonomous control are significantly better than non-autonomous control in terms of both the INTUI and NASA-TLX questionnaires. However, there is no significant difference between the adaptive and fixed semi-autonomous control in the questionnaires in study A. This is despite the performance metrics indicating some significance in at least certain scenarios. It is possible that making the questionnaires more fine-grained, e.g., one for each of the scenarios, would have yielded a significant difference between the adaptive and fixed semi-autonomous control in some cases, similarly to what was observed for the performance metrics. However, such an approach was deemed infeasible as it would require the participants to answer four times as many questionnaires. The INTUI and NASA-TLX results from study B show a more pronounced difference between the adaptive and fixed semi-autonomous control for both questionnaires in comparison to study A. This may be altered slightly if the learning of using the ITCI had been completed as further learning takes place after the currently used 3-5 days. This difference lacks statistical significance, but it could indicate that users without much training, i.e., study B, prefer the fixed semi-autonomous control, whereas there is no clear preference between the adaptive and fixed semi-autonomous control for users with more training, i.e., study A. The difference between study A and B in terms of the questionnaires could once again be related to a combination of less training for participants in study B and the age difference between the two groups of participants. This could indicate that it would have been beneficial to run study B using the same structure as study A and preferably with a younger age group. However, at the time when study B was conducted, this was not possible, but it is something to keep in mind for future studies. Finally, the current system relies on classic image processing techniques when performing object detection for the sake of producing reliable results in a controlled environment.