Subjective aspects of mental workload in air-traffic control Christian COLLET1 CA, Philippe AVERTY2, Georges DELHOMME3, André DITTMAR3 and Evelyne VERNET-MAURY3 1. Laboratoire de la Performance – UFR STAPS - Université Claude Bernard, 27, 29 Boulevard du 11 Novembre 1918 - F-69622 Villeurbanne Cedex, France. Tel : 00 33 4 72 43 28 42 Fax : 00 33 4 72 43 28 46 Email :
[email protected] 2. Centre National de la Navigation Aérienne, 7, avenue E. Belin, BP 4005, 31055 Toulouse Cedex France. 3. Microcapteurs et Microsystèmes Biomédicaux, INSA Lyon, CNRS UMR 5511. 20, Avenue Albert Einstein, F-69621 Villeurbanne Cedex, France CA
: corresponding author: Abstract : Two indices are usually used to estimate air-traffic controllers’ workload: subjective self estimation and objective estimation by counting aircraft to be monitored. However, due to uncertainty and real time pressure, the strain undergone by the controller remains difficult to evaluate objectively. The aim of this paper is to test a new index integrating both, traffic features influencing workload (monitoring, radar regulation and conflict solving) and the number of aircraft. This Demand Index (DI) was computed every ten seconds during real traffic work sessions of one hour involving 25 professional air-traffic controllers (Saint Exupéry airport, Lyon, France). Five vegetative variables (skin potential and conductance, skin blood flow and temperature and instantaneous heart rate) were recorded. Tonic level variation across time was used to evaluate activation level changes every ten seconds. After the session, each controller was asked to evaluate his own workload using the NASA TLX rating scale. If subjective self-estimation was correlated to DI, DI revealed stronger correlation with vegetative activity than the number of aircraft. Any increase in DI was closely associated with vegetative variation. Thus, emotional activity, i.e. workload subjective aspects, should be taken into account in the evaluation of workload.
1. Introduction The wide use of control and display technologies in modern industry requires new approaches to mental workload assessment as a pre-condition for the definition of working conditions and prevention of negative impacts, as these could be associated with catastrophic effects. Although there is no universally accepted definition of mental workload, a recent consensus suggests that mental workload can be conceptualised as the interaction between the structure of systems and tasks, on the one hand, and the capabilities, motivation and state of the human operator, on the other [1]. More specifically, mental workload has been defined as the "cost" a human operator incurs as tasks are performed. According to Gaillard [2], mental load and stress are related concepts that originate from different theoretical frameworks. The second (strain) is of a more subjective nature and represents the “cost” for the operator, i. e. the effects of previous constraints undergone: there is general agreement to consider that strain is the actual workload. Given the multidimensional nature of mental workload, no single measurement technique can be expected to account for all the important aspects of human mental workload.
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Different possible physiological approaches were discussed by Kramer [3] and Wilson [4]. Central nervous system techniques mainly used are electro-encephalographic activity (EEG – [5]), event-related potentials (ERP - [6, 7]), magneto-encephalography (MEG), and
brain metabolism measurements (PET). Measurements of these different forms of cerebral activity remain somewhat difficult to obtain in the workplace (actual working conditions). In a simulated air-traffic control task, Brookings et al. [8] evidenced that EEG was associated with main effects for the type of traffic: number of aircraft to be handled, traffic complexity and time pressure. Other measurements such as pupil diameter, eye blink and electro-oculography were used, but only in a limited field of specific applications. With regard to peripheral techniques, measurement of cardiac activity has been the most popular physiological technique employed in the assessment of mental workload, both from tonic variations in heart rate [9, 10, 11, 12] and after treatment of the cardiac signal [13, 14]. Literature seems to suggest that certain components of heart rate variability (HRV) exhibit systematic and reliable relationships with task demands [15, 16, 17]. HRV responds rapidly to changes in operator workload and strategies (from several hundred milliseconds to several seconds). According to Jorna [18], HRV is a promising measurement, being however more complex to assess and therefore less often used, especially in dynamic task environments. Furthermore, HRV seems to be distorted by respiration [19]. According to Kramer [3], the main purely peripheral expression of ANS response is known to be electrodermal activity (EDA). Early interest in EDA was attributable to its sensitivity to changes in emotion and arousal, a window to the unconscious experiment. EDA can be characterised both in terms of its baseline (tonic level) and its phasic response to an environmental event. Measurement of ANS activity can provide pertinent information on mental workload [20] according to the simplified three-arousal model for psychophysiological reactions in the workplace [21]. This model depicts the suggested brain mechanisms underlying psychophysiological reactions by integrating ideas from i) Pribram and McGuinness [22], ii) Gray [23], and iii) Fowles [24]. These are related to three kinds of arousal processes and their physiological and behavioral concomitants. Arousal system 1 can be referred to as general arousal, system 2 as emotional or affective arousal and system 3 as preparatory or goal-oriented arousal. These 3 systems are influenced by ANS functioning : system 1 through its tonic evolution during the task, system 2 through its phasic responses and system 3 through its well-known anticipatory function preparing the subject to optimise behavioral output. A major point is that when EDA can be recorded continuously, a potential for providing measurements that respond quickly to phasic shifts in mental workload is offered. The aim of the present study is to evaluate mental load during real sessions of air-traffic control by using both the number of aircraft to be monitored and a new workload index (named DI i.e. demand index). Due to the multifaceted nature of mental workload, multiple physiological measurements are required as these provide a comprehensive picture of the mental demands of a task [10]. Thus, ANS activity was recorded through 3 different types of measurements : electrodermal (skin conductance and potential), thermovascular (skin blood flow and temperature) and cardiac (instantaneous heart rate). The purpose of the study was to emphasise the simultaneous variations in workload and neurovegetative physiological parameters. The methods to be used could be summarised : i) by measuring air traffic controllers'autonomic activity and its variation through time. The recording of ANS variables was assumed to attest activation variation through time. ii) by comparing the correlation between autonomic activity variations and DI on the one hand and another objective index (the number of aircraft to be monitored) on the other. iii) by comparing DI and a subjective index (evaluation by the controller of his own workload, with the NASA-TLX test). 2
2. Task and Method The experiment took place at Saint Exupéry airport (Lyon-France) and was designed to evaluate air-traffic controllers'workload. Twenty five air traffic controllers were tested on their own work site in real conditions (in front of their radar screen). Each controller was tested for about one hour toward the end of the evening (between 6 p.m. and 9 p.m.). Prior approval from the French National Airline Center was obtained. Signed informed consent was obtained from each of the 25 controllers after they had been fully informed of the nature of the experiment. The procedures followed were in accordance with the ethical standards of the national committee responsible for human experimentation. Due to the great majority of male subjects in the field of air-traffic control, only one female took part in the experiment. When the number of aircraft flying within an air traffic controller' s sector at a given moment is chosen as the index for mental workload evaluation, each aircraft is attributed the same coefficient, regardless of its position in the traffic and its interaction with other aircraft. In this study, a distinction was made between different traffic configurations according to the attentional resources allocated by the controller to each aircraft [25]. Three situations were distinguished and considered to influence demand index computation. • Monitoring: aircraft routes are as planned and only require supervision. The controller' s task is to verify that each aircraft' s parameters remain within standard intervals (minimal safety rules : vertical separation, 1000 feet; lateral separation, 3 nautical miles). • Radar regulation: all aircraft heading for the same airport need to be separated by a minimal safety interval. The controller must ensure that aircraft maintain a minimal distance exceeding the safety interval. In this case, orders need to be given to pilots to regulate routing. • Potential collision: aircraft routes must be estimated in anticipation by the controller to fall within safety intervals. These thus require route modification. Workload is thought to increase between the first (monitoring) and third (potential collision) situations and was respectively quantified 1, 2 and 3.5. Two additional factors were taken into account : gravity and emergency. When two aircraft were in potential collision, gravity was related to the separation distance between them according to the minimal separation rule. Emergency was related to the time latitude which allows the controller to find a solution; emergency was particularly related to potential collision. To summarise, demand index was defined as the strain, i.e. the amount of attentional resources mobilised by a controller responsible for numerous aircraft at a given time. The index was calculated using : • the number of aircraft and the decision related to each of these. • the necessity for anticipation, in order to modify some aircraft routes (gravity). • the deadline before which the controller was to act (emergency). Table 1 gives an example of the demand index computation method. Table 1 : Example of computing Demand Index (DI). DI is the sum of the number of aircraft (N) and the additional load computed from the configuration of each pair of aircraft. Additional load may originate from radar regulation (Ri), conflict (Ci) or both. In this example, and according to this method of workload computing, DI is higher in the 18:24:50 situation than in the 18:24:10 situation. However, only five aircraft require monitoring in the first situation compared to seven in the second.
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TIME 18:24:10 18:24:20 18:24:30 18:24:40 18:24:50
C1 1.2 1.3 1.5 1.7 1.8
C2
C3
0.6 1.3 1.9
0.1 0.3 0.4
R1 2.0 2.0 2.0 2.0 2.0
R2 0.7 0.1 0.3 0.5 0.7
R3 0.8 0.8 0.9 0.9
N 7 6 6 6 5
DI 10.9 10.2 11.3 12.7 12.7
This experiment was based upon the hypothesis that the impact of each aircraft on the controller' s mental workload was dependent upon operations required : monitoring, regulation or conflict (potential collision). According to professional controllers, some potential collision situations must be weighed up with respect to the probability of aircraft flying at less than the minimal safety interval. Thus, a highly probable potential collision will be graded higher than a potential collision with low probability [25]. Finally, the index representing the workload at a given moment was composed of the amount of the indices attributed to all aircraft at that time. For n aircraft, the demand index (DI) becomes : DI = Σ DI(j) with 1 ≤ j ≤ n The demand index was computed every 10 seconds and sections of the experiments were then divided into five groups as follows: Group I: work session when DI was ≤ 2.5 possibly inducing hypovigilance. Group II: work session when 2.5 < DI ≤ 5.5 corresponded to standard work. Group III: work session when 5.5 < DI ≤ 9.5 corresponded to medium workload. Group IV: work session when 9.5 < DI ≤ 14 corresponded to high workload. Group V: work session when DI > 14 was related to excessive workload risk. 3. Physiological parameters Five physiological parameters were continuously and simultaneously recorded during all working sessions: Skin potential, Skin Conductance, Skin blood flow, Skin temperature and Instantaneous heart rate. Tonic levels were assumed to vary with workload. The controller was seated in front of the radar screen and worked under normal conditions. Thus, the experiment took place in the field. 3.1. Skin potential (SP) Skin potential was recorded using Beckman self-adhesive 78 mm² Ag/AgCl electrodes. Electrode positioning was in compliance with traditional recommendations [26]. The active electrode was placed on the hypothenar eminence of the subject' s non-dominant hand after alcohol-ether cleaning of the skin. The reference electrode was placed 10 cm higher on the wrist. 3.2. Skin conductance (SC) Skin conductance was recorded using 50 mm² unpolarisable Ag/AgCl electrodes (Clark Electromedical Instruments) placed on the second phalanx of the index and the third digit of the non-dominant hand, held by adhesive tape. Skin conductance was measured using 15 µA DC current. In order to eliminate interference between skin potential and conductance and other artefacts, a high-rate common-rejection mode differential isolation amplifier (Analog Devices AD 293A) was used. Likewise, recorded inputs were in a different mode and the conductance supply circuit was of the floating type. Current for skin 4
conductance measurement passed between the index and the third digit while skin potential was measured between the hypothenar eminence and the inner side of the forearm [26]. Electrodermal activity is positively correlated with workload: an increase in task demands elicits an increase in skin conductance and in skin potential. 3.3. Skin blood flow (SBF) This parameter was measured using the HEMATRON patented sensor (CNRS/ANVAR, Patent n° 85/15932) [27]. The non-invasive sensor was placed on the skin, on the thenar eminence of the non-dominant hand and held with adhesive tape. The sensor consists of a disc 25 mm in diameter and 4 mm thick. The active part of the sensor in contact with the skin is made up of two parts : a reference area at the periphery of the disc and a measurement area at the center. The temperature difference between these two areas is measured using 16 thermocouple junctions. A very low thermal flat inertia heater is located in the central part of the disc. A proportional, integral and derivative control regulates electrical heating power to maintain a constant temperature difference of 2°C between the central area and the periphery. The size and shape of the heater are designed in such a manner that a thermal field is propagated only in the capillary network. The power necessary to maintain the temperature difference constant depends on skin blood flow: heat is transferred through the skin and washed out by the blood flow. At all times, electric power is directly proportional to the heat evacuated by the tissue blood flow [28]. 3.4. Superficial skin temperature (ST) This was measured by a low inertia thermistor (Betatherm 10 K3 MC D2). The 4 mm² sensor was fixed on the middle of the palm of the non-dominant hand with non-caustic glue. Under such conditions, the sensitivity threshold for temperature detection by the sensor and its associated instrumentation was lower than one hundredth of a degree. Skin thermo-vascular activity is linked with workload: increases in task demands elicit decreases in skin blood flow and skin temperature. 3.5. Instantaneous heart rate (IHR) 3 silver electrodes were used in the precordial position to measure the ECG. The D2 derivation signal (the interval between 2 consecutive R waves of the ECG) was processed electronically and delivered in the form of instantaneous heart rate. The smallest appreciable variation was 0.5 of a beat per minute and the calibrated scale ranged from 0 to 200 beats per minute. By this method, heart rate increase or decrease can be easily detected and quantified. Cardiac activity is linked with workload: increases in task demands elicit increases in heart rate. 3.6. Physiological variable recordings Data were recorded on a computer (Toshiba T3200) using a 16-bit high resolution data acquisition board (ADAC 5508HR). Data acquisition software was specifically developed to meet the hardware requirements of the system. Another special software package was designed and developed for data processing. A digital signal-processing library of functions was added to the software to eliminate artefacts and to filter out random noise whenever present on the recorded signals. An analog paper recording (YTSE 460 type BBC Brown Boveri), fitted with an event tracer and an automatic synchronisation appliance which cancelled out temporal differences between the markers, was used in parallel for rapid 5
control of measurement quality in real time. After being recorded continuously, physiological values were averaged every ten seconds to be related to workload computation. 3.7. Index load computation Three methods were used to quantify aircraft controllers'mental workload. First, demand index (DI) values were computed every 10 seconds throughout the experiment according to the above-mentioned rules (number of aircraft being monitored, regulated, or in potential collision with weigh-up according to gravity and emergency levels [25]. Second, the number of aircraft (NBA) under supervision was used as the common traditional index of workload measurement in air-traffic control. In the same way, values were quantified every ten seconds. Third, using the NASA TLX test, subjects were asked to quantify the workload themselves, according to their own feelings. The value computed over a given period was repeated every ten seconds, in order to make comparisons possible. 3.8. Data processing To establish a relationship between autonomic nervous system tonic responses and the demand index, neurovegetative values have been classified under one of the 5 groups corresponding to each controller' s workload level, at different times of the experiment. Computations have been made for each autonomic variable. In view of the great interindividual variability of neurovegetative values, normalised data have been used. The basal value of each physiological variable was recorded at rest, i.e. before the beginning of each work session. Values recorded under the 5 different workload levels were then referred to the mean basal value. Thus, each physiological variable is expressed through a ratio. As skin temperature and skin blood flow decrease simultaneously with workload increase, ratios were less than 1. Conversely, as skin conductance, skin potential and heart rate increase when workload increases, ratios were higher than 1. Coefficient correlation computation has been carried out using the Bravais-Pearson test. 4. Results During the experiment, all subjects worked between workload levels 1 and 3. Of the 25 subjects, 10 worked up to level 4 and 10 subjects occasionally worked up to maximum DI (level 5). When workload increased objectively (i.e. increase in DI or in number of aircraft), subjects were considered as being activated when physiological variables showed increasing arousal. Conversely, subjects were considered to be relaxing when physiological variables indicated a decrease in arousal. Thus, subjects were considered to be in stable arousal when physiological variables remained stable. Physiological and load index relationships are shown in Table II. The third workload indices showed clear relationships with neurovegetative variables i.e. a majority of subjects increasing their activation when workload increases in parallel, thus attesting their reliability. However, the percentage of activation is greater when DI is considered, rather than the number of aircraft, whatever the physiological variables. Skin conductance and Instantaneous Heart Rate appear to be the most correlated with Demand Index, number of Aircraft and Self-estimation. Conversely, thermovascular variables seem less reliable.
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Table II: Changes in physiological variables as a function of different workload indices (Demand Index, Number of Aircraft and Self estimation). Percentage of subjects showing activation, relaxation or remaining stable when workload increases. Such variations are attested by changes in vegetative tonic activity (physiological indicator). ACT = activation, REL = relaxation, STA = remained stable. SC = skin conductance, SP = skin potential, ST = skin temperature, SBF = skin blood flow, IHR = instantaneous heart rate.
Physiological indicator
Increasing workload indicator
SC SP ST SBF IHR
DEMAND INDEX
NUMBER OF AIRCRAFT
SELF-ESTIMATION
ACT
REL
STA
ACT
REL
STA
ACT
REL
STA
83.4 68 52.4 56 91.6
8.3 20 14.3 24 4.2
8.3 12.5 33.3 20 4.2
70.9 56.2 47.6 48 87.5
8.3 25 23.8 28 0
20.8 20.8 28.6 24 12.5
62.5 54 23.8 24 66.7
4.2 14 28.6 16 0
33.3 32 47.6 60 33.3
4.1. Estimation of workload by NASA TLX test Relationships between DI and self-estimation are clearly correlated, as indicated by Figure 1. As expected, DI was better correlated with the self-rated workload (r² = .57
– Bravais-Pearson correlation test) compared to the number of aircraft (r² = .47).
4.2. Correlation between autonomic variables (tonic level variation) and workload variation
Subjective self estimation (TLX)
Skin conductance was recorded in 24 subjects from 25. A positive correlation was shown between DI and skin conductance. Skin conductance increased in 83.3 % of subjects while an increase in DI was recorded. Subjects'activation paralleled increases in workload, as shown by Figure 2. 70 Relationships between NASA TLX and DI
y = 9.835x + 12.513
60 50 40 30 20
DI = 1
DI = 2
DI = 3
DI = 4
DI = 5
Figure 1: relationship between DI and self-estimation of workload. Increased workload is clearly perceived by controllers even if their judgement is less accurate than through DI.
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1.7 Normalised values
1.6 1.5
Skin conductance tonic level change y = 0.09x + 0.87
1.4 1.3 1.2 1.1 1
BASAL
DI 1
DI 2
DI 3
DI 4
DI 5
Figure 2: Positive correlation between skin conductance tonic level (SC) and DI. SC presents higher values for high DI values indicating an increased activation level. A linear relationship is established between DI and SC tonic level.
A positive correlation was evidenced between DI and skin potential (SP) as well as between DI and instantaneous heart rate, thus indicating an increase in activation simultaneously with an increase in workload. 68 % presented a skin potential evolution which paralleled that of the index load and 91.6 % showed increasing heart rate when the index load increased. Relationships between Heart rate and DI are shown in Figure 3.
Normalised values
1,25
Heart rate tonic level change
1,2
y = 0.05x + 0.9
1,15 1,1
1,05 1 BASAL
DI 1
DI 2
DI 3
DI 4
DI 5
Figure 3: Positive correlation between instantaneous heart rate and demand index. Heart rate presents higher values for high demand index values, indicating an increased activation level. A linear relationship is established between DI and heart rate.
A negative correlation was evidenced between DI and skin blood flow whereas skin temperature was not shown to evolve in parallel with DI. 14 subjects (56 %) presented a decreased skin blood flow while their index load increased, whereas only 11 subjects (52.4 %) showed decreased skin temperature while their demand index increased. Relationships between skin blood flow and DI are shown in Figure 4.
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1
Skin Blood Flow tonic level change
Normalised values
0.98 y = -0.02x + 1
0.96 0.94 0.92 0.9 0.88
BASAL
DI 1
DI 2
DI 3
DI 4
DI 5
Figure 4: Negative correlation between skin blood flow and demand index. Skin blood flow presents lower values for high demand index values, indicating an increased activation level. A linear relationship is established between DI and skin blood flow.
The general evolution of slopes from low to high workload levels seems identical whatever the physiological variable (with the exception of Skin Temperature).
5. Discussion This experiment involved the measurement of three workload indices. The NASA TLX test gave subjective information on mental workload, estimated by the controllers themselves. The number of aircraft (NBA) index gave a quantitative estimation of mental workload by direct objective evaluation of the number of aircraft to be monitored. The number of objects classified is one of the most common methods for estimation of workload associated with a task. In air-traffic control, the number of aircraft the controller is in charge of has often been considered a reasonable representation of air-traffic control workload [29]. Observation of a controller managing a number of aircraft in a given ATC sector tends to show that N is not a perfect index [30], especially as aircraft configurations (the way these are spread over space and time) heavily bias it [31]. This hypothesis seems to be confirmed by the present study. Subjective estimation by questionnaires represents a relatively simple way to quantify mental workload. The NASA-TLX rating scale has been used to assess the load the controller estimated to have undergone, after the observation [8, 32]. To do this, each controller has to quantify his workload, using a specific scale. However, evaluating workload after the work session could lead to rough approximation. The demand index (DI) was based on the number of aircraft, but in addition, integrated a subjective estimation of mental workload. It thus appears to be more reliable. With reference to Table II, the proportion of subjects showing a correlation between one of the three workload indices and autonomic variables, thus attesting to activation, is different according to each index. DI is better correlated to physiological values than to NBA and the NASA TLX test. The NASA TLX test gives the lowest results. Thus, DI is closer to the controllers'actual activation. Skin conductance and instantaneous heart rate are the most reliable variables: as the workload increases, they are better related to this activation than skin potential, and skin blood flow. In this experiment, skin temperature was shown to be the poorest indicator.
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By taking regulation and potential collision into account, an emotional aspect of workload is thus integrated to DI quantification. Indeed, a potential collision situation means a possible crash and thus requires rapid analysis and decision on the part of the controller. This situation is quite different from monitoring and represents an additional demand on mental resources, involving emotional activity, thus making workload increase drastically. The emotional load dimension could thus be proposed as a functional, subjective part of workload [33]. Emotional load is particularly related to critical situations (regulation, potential collision) and should be taken into account in workload quantification. The increase in activation was paralleled by ANS variable evolution thus attesting the efficiency of criteria integrated in demand index in addition to the number of aircraft : potential collision gravity and emergency. As supposed, an increase in demand index is not only associated with the processing of information, but also with the strain undergone by the controller due to uncertainty and emergency in several monitoring situations. DI has been established according to expert controller experience. It was estimated that a work session could be distributed along five levels: these are commonly occurring situations. However, traffic differentiation based on 4 levels is almost as acceptable: estimations regarding low, medium, high workload and overload seem to be close to real situations, as autonomic variables do not often differentiate DI 4 from DI 5, according to each individual. Such a distinction seems reliable when the whole experimental population is considered. With a view to helping controllers in decision making, an objective method of workload quantification is needed. However, this decision must be taken on the basis of objective data, whereas emotional load elicited by potential collision situations could be different, according to each individual. Controllers'resistance to stress should be taken into account. Taking into account this emotional factor in DI computation could be considered the first step toward future directions for research. Linear relationships have been evidenced between vegetative and DI variations. However, some differences may be established among subjects: some controllers could show increased activation as soon as they begin to work, and maintain this activation level throughout their work session. Others maintain a low level of activation at low workload level and increase their activation in order to upgrade their efficiency to medium level. Likewise, it is perhaps unnecessary to increase activation between levels 3 and 5. Several monitoring models could thus be evidenced and activation typologies could be drawn up. In conclusion, recent findings [34] have shown the high weight of emotional factors in information processing and decision making. DI could thus bring more information than NBA and be a reliable factor used to limit risk. It should be possible, in the future, to have such an index computed by an expert system to be provided automatically and directly to prevent excessive workload.
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