Keywords

1 Introduction

Alertness refers to the ability of an organism to maintain attention and remain vigilant during long periods of time [23]. Maintaining alertness in personnel is a vital part of ensuring safety in production, especially in the field of aviation, in which flight safety must be guaranteed at all times. The importance of alertness in aviation has been demonstrated by numerous flight accidents, such as the Guantánamo air disaster (1993), the Korean Air Flight 801 disaster (1997), and the Air Berlin pan pan distress call made because of pilot fatigue while approaching Munich, Germany (2012). Caldwell et al. considered that the short sleep time, the early awakening and jet lag could reduce the alertness, flight ability and lead to fatigue of pilots [8]; Blakey also pointed out that sleep time, circadian rhythm, and sleep quality affect the alertness of pilots [17]. Sleep time will be taken up by flight duration and caused fatigue of pilots, especially the long-range pilots: long-range flight have longer flight duration, combined with the effect of jet lag and cabin conditions (such as: light, temperature, noise, turbulence and so on), make pilots to stay awake for a long time. For example, 25 of the 392 long-range flights from Singapore to New York reported no sleep record of pilots in 2005 [1]. It is obvious that quantizing the alertness of pilots, studying its change rule during continuous wakefulness, and undertaking necessary measures to deal with decreased alertness are very important in increasing aviation safety and decreasing accident rates.

Borbe’ly made early studies of the level of alertness of human body over the course of a day, and he thought that alertness depends on homeostatic and circadian drives of the body [18]. Monk et al. discussed the factors influencing human performance by measuring the secretion of human hormones, arguing that circadian rhythms have an impact on job performance [19]. Akerstedt and Folkard proposed the Three-Process Model of Alertness, which elaborates further on the role of circadian and homeostatic mechanisms in alertness under conditions of continuous conscious wakefulness [3]. Circadian rhythms refers to the rhythms of human physiological activities, and they are an evolutionary mechanism that enable the human body to adapt to long-term choices of environment [2]. They are usually expressed as a 24-h sinusoidal curve; however, because of individual differences, there is no exact representation. They generally show a rise from 6:00 a.m. to 18:00 p.m., peaking at 18:00, and declining from 18:00 to 6:00 [12]. The homeostatic mechanism is closely related to the amount of sleep, and it maintains alertness. Its value is lowest during the wake-up phase, and it gradually increases as time passes after waking up [3].

In the early days of alertness assessment, physiological signals, including blink frequency, skin resistance, body temperature, and blood pressure were used to evaluate alertness [7]. As research methods advanced, alertness assessment focused mainly on the degree of fatigue and subjective consciousness, reaction time, EEG activity, and hormone secretion [9]; e.g., Jung et al. assessed the state of alertness of operators through EEG measurements [13]; Nicholson et al. used EEG measurements to study the night sleep and alertness of a flight crew flying from London to San Francisco [20]; Badia et al. confirmed that alertness is regulated by the circadian rhythm system according to the beta activity of the brain and the body temperature index [4]. Biochemical and physiological signal detection methods have clear indicators and accurate test results, but rigorous testing conditions, complex testing process, and research results are not easy to generalize and apply [10]. Compared to other testing methods, subjective evaluation and bioreaction testing are much more convenient, rapid, and effective approaches for assessing the alertness of personnel. Sallinen et al. applied the Karolinska sleepiness scale (KSS) to evaluate both sleep and duty alertness in long-haul airline transport pilots [22]. The psychomotor vigilance test (PVT), which is an assessment tool based on reaction time, is also used widely by researchers [5, 16, 24]. The above researches mainly focus on the measurement of instantaneous alertness and fail to carry out the alertness analysis based on the information processing ability of the human brain. During the working process of first-line staff such as pilots, the brain needs certain information processing to maintain the work performance, making the above studies have some limitations in the practical application process.

In addition, with the process of brain information processing, nervous activity characteristics will produce a corresponding change [6], and according to the theory of neural activity, neuronal cells in the cerebral cortex have the characteristics of excitation and inhibition, and a combination of changes in these characteristics affects alertness [21]. Different people have different nervous activity and enter the state of fatigue differently [11, 25]. Early foreign scholars used the “Uchida–Klinebrene measurement method” and “Amphim scales” and other methods to measure changes in nerve type, and domestic scholars have also designed the 80.8 Nervous System type measurement to assess the types of nerves in the process of brain information processing [26, 27]. However, such studies mainly focus on the measurement of nervous activity characteristics, failed to be linked with the alertness in the production process, and the maintenance of alertness is of great significance to ensure safety in aviation.

Our study uses the nervous system assessment method to study changes in the alertness of subjects with the process of brain information processing in a 36 h state of wakefulness, more deeply from the perspective of neural function and hope to provide new ideas for the fatigue management of pilots.

2 Materials and Methods

2.1 Subjects

The subjects in this study were 6 young students (22–25 years of age), with an average age of 23.6 years. They were right-handed, and in good health, with healthy lifestyles; e.g., they did not smoke or drink alcohol, did not drink coffee, did not take drugs, and maintained a regular routine. All subjects gave their written informed consent for the study, and the experiment was approved by the Ethics Committee of Civil Aviation University of China.

2.2 Equipment

Nervous System Assessment of the aviation personnel safety risk assessment system involves a fatigue-inducing task and the use of recording equipment to measure reaction time. The Nervous System Assessment Tool is a crossing-out experiment that references the Uchida-Kraepelin test for psychological stress [26], 80.8 Nervous System type measurement [27], and the BTL-QZ Test [15] and is based on the visual–action conditional reflex and performance testing principles. In the course of experimental operation, the subjects’ nervous system activities are continuously in a state of conversion of synaptic excitation to inhibition. According to Pavlov’s theory, the operation results are divided into three dimensions: excitation, inhibition, and stability (excitement–inhibition) of the nervous system, which can be realized through a crossing-out experimental design.

Figure 1 shows the visual test interface of software programming; the interface is divided into three areas: (1) The Reading Area consists of 10 symbols and is divided into 6 rows, each of which consists of 20 symbols freely combined, making a total of 120 symbols{}; (2) The Marking Area consists of 2 target symbols (these appear at random and are represented by the following symbols: {}) and a Judgment symbol{}; (3) The Operating Area includes three basic operations: Circle, Stroke, and Ignore [14].

Fig. 1.
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Test interface

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The operating rules are shown in Table 1.

Table 1. Operating rules
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The process of the operation rules designed by the tester is shown in Fig. 2.

Fig. 2.
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Operating rules’ flow chart

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According to the distribution and meaning of theoretical and practical results, measurement results is defined as follows: D/D-R is the reaction time under nervous excitation; C/C-R is the reaction time under nervous stability; and I/I-R is the reaction time under nervous inhibition. This tool has been demonstrated in the correlation between human error and the characteristics of nervous system activity [14].

2.3 Procedures

Six subjects were asked to ensure that they had normal sleep the day before the experiment and to enter the laboratory at 8 am on the first day for continuous sleep deprivation (SD) of 36 h. The reaction time data collected during the 36 h were used to evaluate the status of alertness of the participants during continuous wakefulness. During the 36 h, the participants took a unit of 1 h as the assessment cycle. They simulated the control task for 40 min, and the experimental data were collected for 6 min, and the remaining 14 min were used to adjust the distribution. During the experiment, there was constant supervision by the managers to prevent participants either from touching any stimuli or falling asleep.

2.4 Analytical Approach

The database was established by using spss22.0, and descriptive statistics and ANOVA were also carried out to analyze the characteristics of and differences between the three types of reaction time. We used OriginPro 2017 to make drawings and conducted Polynomial Fit between sleep deprivation time and reaction time.

Alertness analysis was based on the reaction time in 36 h sleep deprivation time and was assessed using three different ways: Fluctuation trend in 36 h of 6 subjects’ reaction time by the way of drawing Grouped Box Charts; The difference of reaction time in three sleep deprivation time period (1–12 h, 12–24 h, 24–36 h) by the way of analyzing mean, standard deviation and ANOVA, and the result was shown by mean±standard deviation in table; Mathematical relations between reaction time and sleep deprivation time by Polynomial Fitting.

3 Results

As shown in Figs. 3, 4 and 5, the Grouped Box Charts were made using 3 h time-periods to show the reaction time of the six participants under the three types of nervous effect over 36 h. Ignoring the learning effect in the first three hours (the first time period), it can be seen from the figure that the reaction time fluctuates with time and that there are abnormal values with a large degree of deviation.

Fig. 3.
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D/D-R of 6 subjects during 12 time periods

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Fig. 4.
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C/C-R of 6 subjects during 12 time periods

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Fig. 5.
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I/I-R of 6 subjects during 12 time periods

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From the result of mean (as shown in Table 2), three kinds of reaction time have the lowest reaction in 12–24 h; compared with the two time period between 1–12 h and 24–36 h, the D/D-R and C/C-R decreased, while I/I-R increased after 24 h sleep deprivation. According to the results of standard deviation, the discrete degree of three kind reaction time were decrease by the time. Through the descriptive statistics and ANOVA of the mean of D/D-R, C/C-R and I/I-R of the six subjects (as shown in Table 3), we found that D/D-N, C/C-N, I/I-N, C/C-R and I/I-R were significantly different in three time periods at the confidence level of 0.01; D/D-R was significantly different in three time periods at the confidence level of 0.05.

Table 2. Results of descriptive statistics and ANOVA in three sleep-deprivation time-periods
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Table 3. Parameters of Polynomial Fit
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According to the Box Chart distribution characteristics of each indicator, the greater dispersion degree data (outlier) of the six subjects are eliminated. The Polynomial Fit was made by using the mean of D/D-R, C/C-R and I/I-R of the six subjects as the dependent variable and the SD time as an independent variable. The Parameters, Statistics and ANOVA of the Polynomial Fit are shown in Tables 3 and 4, and the Fitted Curves Plot is shown in Fig. 6.

Table 4. Statistics and ANOVA of Polynomial Fit
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Fig. 6.
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Fitted curves plot of reaction time

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The fluctuation trend of each indicator in 36 h is shown in the Fig. 6.

The Fitting result between C/C-R and SD can explain 82.2% of the variation rate, which was significant according to ANOVA (F = 33.548, P < 0.01). The fitting model is:

$$ {\text{Y}} = 1498.667 + 65.253{\text{x}} - 10.941{\text{x}}^{2} + 0.480{\text{x}}^{3} - 0.006{\text{x}}^{4} ; $$

The Fitting result between D/D-R and SD can explain 81.4% of the variation rate, which was significant according to ANOVA (F = 42.831, P < 0.01). The fitting model is:

$$ {\text{Y}} = 2149.563 - 103.032{\text{x}} + 5.636{\text{x}}^{2} - 0.087{\text{x}}^{3} ; $$

The Fitting result between I/I-R and SD can explain 85.6% of the variation rate, which was significant according to ANOVA (F = 59.221, P < 0.01). The fitting model is:

$$ {\text{Y}} = 1596.763 - 89.719{\text{x}} + 5.814{\text{x}}^{2} - 0.093{\text{x}}^{3} ; $$

Excluding the learning effect on the reaction time of first two groups, there were differences among the responses of the three neurological fluctuations during the 36 h. D/D-R showed the highest reaction time, while the difference in reaction times between I/I-R and C/C-R was small. D/D-R increased from 8:00 to 19:00 under the influence of circadian rhythms and showed the slowest reaction time around 19:00. Under the effect of homeostatic driving and circadian rhythms, D/D-R showed the fastest rate of increase from 12:00 to 6:00, with the upward trend disappearing around 15:00 the next day. With the increase of circadian rhythms after 15:00, D/D-R decreased to a certain degree. I/I-R will be earlier into the state of fatigue: from 8:00 to 17:00, I/I-R decreased. Compared to D/D-R, which showed its slowest reaction time at about 19:00, I/I-R reached its minimum reaction time at 17:00; after 17:00 the I/I-R increased and the upward trend disappeared around 15:00 the next day. Thus, the period of increase of I/I-R is longer than that of D/D-R. C/C-R will be later into the state of fatigue: C/C-R decreased from 10:00 to 3:00. Compared to D/D-R, which reached its slowest reaction time at about 19:00, I/I-R reached its slowest reaction time at about 3:00 the next day. Thus, the period of increase is shorter for I/I-R.

4 Discussion

Previous researches on the alertness of pilots were mostly based on treatise or interviewing pilots [8, 17], even though some researches use the measure of experiments, the data of which is just about pre-flight and post-flight [5, 20, 22], that will miss the alertness of pilots during the specific implementation of the flight, and the corresponding fatigue management measures lack persuasion, can not be specific and detailed guidance flight mission. Our research study the alertness of subjects during continuous 36 h, can effectively make up the lack of previous researches, and for the first time from the perspective of nervous activity to study alertness of the pilots, can propose more innovative and effective pilot management measures.

The experimental results confirm that circadian and homeostatic driving have an influence on the alertness of personnel [3, 18, 19]. Especially in the late night from 3:00–6:00, when the circadian and homeostatic drive have negative impact on the alertness at the same time: the reaction time of three nervous activity all have the fastest rate of rise, and subjects are difficult to focus and maintain the work performance during this period in practical work. What’s more, the time under different nervous activity enter the state of fatigue are different. According to the results of the experiment, alertness under the effect of nervous inhibition maintain the state of conflict (which can be confirmed by the larger slope of the curve and the large difference between the front and back time periods in Fig. 6), with a earlier decline, and shorter time into the state of fatigue. Under the effect of nervous inhibition, subjects unable to maintain work performance for a long time. Nervous stability can reflect the stability of the individual [15], and according to the fitted curve results (Fig. 6), there is a steady trend and lower differences in the later time period, indicating that nervous stability have stronger toughness compare to other types of nerves and a longer time to maintain alertness. Under the effect of nervous stability, subjects able to maintain a longer working ability.

The application of current research findings might be able to integrate to airlines safety management systems for pilots’ rostering. For example: the pilots have a stronger nervous stability can have a stronger ability to continue working in a long time who can be selected for long-range flight and night flight, while the pilots have a stronger nervous inhibition who will be earlier into the state of fatigue and have a suddenly alert dropped in night, are not suitable for long hours of continuous flight, should be avoided carrying out long-range flight and night flight. In general, pilots should be arranged night shift as few as possible, they are difficult to focus and maintain performance in night time; If night work can not be avoided, pilots have to ensure adequate sleep before performing night shifts, which can greatly help to improve alertness [8]; And multiple pilots should be set to strengthen the work rotation in long-range flight, to avoid a sharp decline of alertness on the ability to fly in long awakening time.

This study focuses on the change of the alertness of the general public during continuous wakefulness and does not divide according to the types of circadian rhythms. However, the alert performance of pilots with different rhythmic types will be different [2]. Subsequent studies should refine the impact of rhythmic patterns on alertness and provide more detailed advice on flight fatigue management.

5 Conclusion

Based on a 36 h sleep-deprivation experiment, reaction time under the influence of nervous excitation, nervous inhibition, and nervous stability were quantitatively analyzed by Polynomial Fit. We drew the following conclusions: (1) Alertness under different nervous activities is significantly affected by the circadian and homeostatic drives. In general, alertness increases from the point of morning and reaches its highest point around nightfall. It declines most rapidly at midnight and minimum alertness is reached around 15:00 the next day. (2) Alertness varies under different states of nervous activity: under nervous inhibition, reaction time enters the fatigue state earlier and shows a longer period of decline, and with a weaker ability to maintain work performance in a long time; under nervous stability, reaction time enters the state of fatigue later, and the period of decline in alertness is shorter, and it with a stronger ability to maintain work performance for a long time. This study provides a new perspective of measuring alertness and of managing and controlling pilots fatigue to some extent, and the study may help the pilots’ rostering according to the difference of nervous activity which is meaningful for the safety of aviation.