## 4 Results
### Maintenance Task Use-Case Efficiency Validation
#### 4.3.2 Results  
For the MANTRA system analysis use-case, both the subjective appreciation of the participants (through questionnaires) and the objective measurement of efficiency were studied by measuring times and errors committed. Table 6 shows the efficiency results, in terms of time and the number of errors made. As can be seen, there was a significant mean reduction (11%) in time when using AR in the diagnosis phase, a value that improved to 17% when using IRT. This was due to the fact that having IRT images allowed users to carry out some of the diagnostic steps in a faster way by being able to check the operating temperatures of the different elements without having to measure the voltage and/or current with a multimeter. This reason was also reflected in a reduction in errors made. We understand errors as misconfiguration of the multimeter, measuring voltages or currents at the wrong points, getting confused about the tool to use, misdiagnosing the simulated fault, or performing an incorrect repair. IRT made it possible to greatly reduce the use of a multimeter and, therefore, the number of errors made. In the case of repair, detailed and sequential guidance with AR and/or IRT also reduced the time spent, especially among people with little knowledge of electronics. After the repair task, a board functional check was carried out which, in the case of using IRT, was simplified to verify that the temperature image coincides with its normal operation. Figure 12: Users testing the MANTRA system. Figure 13: User experience with AR. Regarding subjective measures, Figure 14 shows the accumulated results of both the NASA TLX test and the SUS test. In the SUS test, depending on the score obtained, a system can be classified as usable (from 68), good (around 70), or excellent (from 85), with 100 being the highest possible score. According to this classification, the manual system was usable, while the systems with AR and IRT were good. Moreover, with the addition of the IRT, this score increased. This may be due to the fact that, thanks to the use of IRT, the user had to physically perform fewer operations with the board. In the case of the NASA test (Figure 14b), the system with IRT was also the best-rated, followed by the AR system and, finally, the manual system. Figure 15 shows the NASA test results as box plots for each of the aspects that imply cognitive load for an operator. Several conclusions can be deducted from these graphs: The first is that there were no significant differences in physical demand as, in this use-case, the physical work was minimal. The second is that when using the manual system, there was a greater variance in all aspects, this may have been due to the fact that the population with great technical experience required little effort, due their familiarity with working with electrical diagrams and data sheets, while the participants with little or no experience had to exert much more significant effort. This was also reflected in the mental demand graph. When using the MANTRA system, the variance was reduced and, in addition, better mean values were obtained in all aspects; being especially significant in the graphs of frustration, effort, and mental demand. Finally, from the performance graph, it can be seen that subjectively better results are obtained with AR + TE, then with AR; these results were coherent with those obtained quantitatively, in terms of the least number of errors and the least time spent. In reference [64], an automatic detection system of faults in electric impact drills is proposed, using a thermographic measurement classification system based on machine learning techniques (Nearest neighbors and backpropagation neural network). The results and conclusions reached in [64] can complement the current study, so future actions will try to use similar techniques to automate the detection of faults from thermographic measurements and take advantage of the potential of AR to guide the maintenance and repair tasks.