Unveiling nuances in data analysis to illuminate marine pilot strain

Abstract

Maritime studies, encompassing a range of disciplines, increasingly rely on advanced data analytics, particularly in the context of navigation. As technology advances, the statistical averaging of large datasets has become a critical component of these analyses. However, recent studies have highlighted discrepancies between statistical predictions and observable realities, especially in high-stress environments like port approach procedures conducted by marine pilots. This study analyzed physiological responses recorded during simulation exercises involving experienced marine pilots. The focus was not on the specific outcomes of the simulations but on the potential faults arising from conventional statistical signal processing, particularly mean-centered approaches. A large dataset of signals was generated, including one signal with a dominant characteristic intentionally designed to introduce imbalance, mimicking the uneven distribution of real-world data. Initial analysis suggested that the average physiological response of the pilots followed an S-shaped curve, indicative of a psycho-physiological reaction to stress. However, further post hoc analysis revealed that this pattern was primarily influenced by a single participant's data. This finding raises concerns about the generalizability of the S-curve as a typical stress response in maritime pilots. The results underscore the limitations of relying solely on conventional statistical methods, such as mean-centered approaches, in interpreting complex datasets. The study calls into question the validity of standardizing data interpretations based on dominant characteristic curves, particularly in environments as intricate as maritime navigation. The research highlights the need for a re-evaluation of these methods to ensure more reliable and nuanced conclusions in maritime studies. This study contributes to the ongoing discourse on data interpretation in maritime research, emphasizing the critical need to re-assess conventional statistical signal processing techniques. By recognizing the potential pitfalls in data generalization, the study advocates for more robust analytical approaches to better capture the complexities of real-world maritime challenges.

Keywords: cognitive load; data averaging; physiological response; pilotage; port approach; risk assessment; simulation.

Figure 1

S shaped mean of six HR curves (left). The dominant characteristic curve determines the shape of the HR mean curve, and the rest contribute much less to the shape. Averaging non-characteristic curves yields no characteristic shape (right, blue), while the average of all six curves has a precise S shape. This may be misleading since only one curve seems to determine the mean curve.

Figure 2

EDA: The orange curve on the left figure is dominant. The right Figure shows the mean of the EDA curve (plotted on the left side). The upper red curve presents the mean of all curves and blue presents the mean without a dominant curve.

Figure 3

Simulated heart rate responses are given with their mean (left). Means of all signals are reproduced (right, red curve) for purposes of comparison.

Figure 4

Simulated EDA are given with their mean (left). Means of all signals are reproduced for purposes of comparison.

Figure 5

Histograms of mean TVs using a bootstrap procedure of simulated HR are given: no dominant curve (left) and with the dominant curve (right). Note that the dominant curve reflects two components on the histogram on the right.

Figure 6

Histograms of sample mean TVs using a bootstrap procedure of simulated EDA are given without the dominant curve (left) and with the dominant curve (right). Again, the histogram has two components as a dominant curve is reflected.