The ability to measure pilot workload, and particularly compensation, is central to understanding the way in which pilot behaviour differs between flight training devices and real-world flight and therefore, the utility of a flight training device. However, the knowledge of how to quantify pilot behaviour in terms of workload remains elusive.
Adoption of the handling qualities methodology throughout this thesis led to the use of two measures of quantitative perceptual fidelity assessment in Chapter 5, namely:
1) The difference in task performance achieved between flight and simulation and;
2) The difference in the pilot's behaviour, specifically task strategy, required to achieve the task.
Measurement of task performance in flying tasks was relatively straight forward as task performance requirements can be defined through the use of ADS-33E-PRF style Mission Task Elements (MTEs) [19]. This performance was quantified for the baseline and modified simulations and then directly compared for fidelity assessment.
Measurement of the pilot's task strategy, and therefore adaptation of this strategy for fidelity assessment, was more difficult. In the handling qualities framework (and thus far in this thesis) pilot task strategy has been quantified in terms of the control attack metrics (see section 2.2.2).
144 Cooper and Harper define pilot compensation as;
“
The measure of additional pilot effort and attention required to maintain a given level of performance in the face of less favourable or deficient vehicle characteristics” [21]
. The total workload is then comprised of;"The workload due to compensation for aircraft deficiencies plus the workload due to the task" [21].
The research detailed in Chapter 5 concluded that the relationship between individual attack metrics and SFRs was not sufficient for criteria to be proposed for fidelity assessment. It was noted that quantification of pilot task strategy adaptation requires metric(s) that reflect control activity in terms of both frequency and magnitude across all active control axes. Figure 6-1 shows a number of sinusoidal control inputs that vary in frequency, amplitude or both. The mean data points are shown on an attack chart in Figure 6-2. In case 1, the pilot maintains the frequency of control inputs but reduces the peak amplitude. This change in workload is captured by the control deflection metric and attack rate metric but not the attack or attack per second metrics. Conversely, if the pilot reduces the frequency of the inputs but maintains peak altitude, this change in workload is captured by all the metrics except the control deflection magnitude. There are also instances where the pilot will apply the same rate of control input and of the same magnitude but will not be continuously active on the controls. This would only be captured by the attack per second metric. The attack rate metric does not capture all different types of compensation as the same attack rate can be applied to generate a lot of small inputs or a few large inputs. In this case however, the attack per second, control deflection and attack would all capture the difference. This confirms that all three attack metrics (fn, η̇̅̅̅̅̅pk and η̅ ) are required to fully reflect the pilot's
level of compensation as changes in pilot compensation can occur in a number of different ways and not metric alone can capture each possibility.
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Figure 6-1 - Different Types of Changes in Workload
Figure 6-2 - Position of Each Test Case on the Attack Chart
Another reason that the control attack metrics may have proved inadequate to reflect pilot opinion is that the way the pilot manipulates the controls may not be the full extent of their workload. There is mental processing that is continuously required to inform the correct manipulation of the controls in response to pilot perception of vestibular, visual and auditory cues. Roscoe proposed a definition of pilot workload that highlights this:
"Pilot workload is the integrated mental and physical effort required to satisfy the perceived demands of a specific flight task" [83]
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Roscoe's definition is widely used in the human factors community for assessment of pilot workload, not only for flying tasks but for combined flight and mission tasks as well. In such cases, workload has been quantified through direct measurement of physiological quantities such as heart rate [84], eye movement [85], and brain activity [86]. However, there are challenges associated with the measurement of physiological parameters including equipment calibration, intrusiveness of equipment and complex post processing.
Another approach used for the measurement of workload is based on the concept of spare capacity [87]. The postulation is that the workload due to the primary task can be measured in terms of the pilot's spare capacity for performing a secondary task in conjunction with the primary task. As the pilot's workload due to the primary task increases, their spare capacity for undertaking a secondary task is reduced. The workload can therefore be assessed by the degree to which the secondary task performance degrades in the dual-task situation relative to when each task is performed independently. Multiple Resource Theory suggests that rather than having a single resource with limited capacity, discrete resources can be allocated as the task demands [88]. Researchers are therefore advised to take care to ensure that a secondary task is designed with discrete stimuli that occupy the same resource pools as the primary task. For example, if the primary task was communicating with air traffic control, the secondary task should be audio dependent with cognitive elements. However, care should also be taken that the secondary task measures do not intrude on the primary task. This can be a significant problem when measuring physiological parameters of the pilot, for example, the use of head mounted eye tracking device may inherently alter the pilot's scan strategy and also alter physiological measures due to the physical and mental stress of wearing the measuring equipment. The secondary task should be designed to ensure that the secondary task performance is sensitive to any variations in primary task workload being assessed.
In response to the spread seen in the simulation trials described in Chapter 5, it was decided to conduct a fundamental experiment to better understand pilot workload in multi-axis tasks. An isolated study was therefore conducted to develop metrics that can reflect pilot opinion of workload and accurately capture differences in this workload. Such metrics could then be used as indicators of perceptual fidelity. The metrics to be developed were the attack metrics that were utilised in Chapter 5.
Due to the multi-axis nature of rotary wing flight, even for relatively simple manoeuvres, it was hypothesised that a control metric that fully reflects pilot workload would need to be
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multi-axis too. To support the development of such a control activity metric based on the control attack metrics an exploratory piloted simulation trial was designed with the following requirements:
1. Determine sensitivity of attack metrics to changes in pilot workload. For the attack metrics to be successful descriptors of pilot strategy, they must be sensitive to changes in workload.
2. Explore how pilot workload is divided amongst axes in a multi-axis flying task: Is workload proportional to the number of axes engaged? Does the pilot work harder in one axis than another? Do pilots perceive different levels of workload in different axes?
3. Assess whether changes in attack metrics, pilot perception of change in workload and changes in measurements of spare capacity agree with one another.
The exploratory trial was split into two phases. The first phase was designed to look specifically at the effect of multi-axis control on pilot workload as well as aid development of multi axis metrics. The aim of the second phase was to further assess the correlation of metrics with subjective opinion and to determine whether measurements of spare capacity agreed with those of control activity and pilot perception. The adapted Precision Hover MTE (detailed in section 5.3) was chosen for examination in this study due to its multi-axis nature. The following section describes the simulation facilities and methodology used in the simulation trials. The results are then presented and discussed with conclusions in the final section of the paper.