Max Mulder

Startle and surprise are known to potentially incapacitate professionals who respond to emergency situations. In the aviation domain, self-management methods to prevent such incapacitation have been introduced for pilots. This study aims to explore the relevance of similar self-management methods tailored for cabin crew, using subjective evaluation by cabin crew and cabin crew instructors. First, a method was designed and refined using interviews and a focus group with nine subject-matter experts, consisting of four steps: protect, reset, check and act. Second, relevance of the method was quasi-experimentally tested by letting 15 cabin crew members apply it in a simulated firefighting scenario. After performing the scenario, they rated whether the method had positive or negative effects, was difficult or easy to use, and they retrospectively rated their perceived stress before and after applying the method. Participants positively rated the method’s effectiveness and usability, and reported the method to significantly reduce perceived stress. Participants provided several suggestions for improving the method, and for approaches to integrate the method more effectively into existing protocols. The outcomes of this study provide direction on the design and use of self-management methods that can help manage startle and surprise in teams.

Air traffic growth leads to increasingly complex traffic situations near large airports. To support air traffic controllers in maintaining safety and improving efficiency, new support tools are needed. This paper presents the Inbound Traffic Support System (ITSS), a visual interface prototype developed in collaboration with the Netherlands’ Air Navigation Service Provider, LVNL. The interface visualizes constraints associated with the merging task and was tested for the South Sector of Amsterdam Airport Schiphol. By portraying potential merging solutions, it helps area controllers anticipate the impact of their decisions and supports their role in managing inbound traffic. Professional controllers provided positive feedback during the development phase while also identifying concerns such as display clutter that should be addressed. An experiment with semiprofessional controllers assessed the impact of the interface on operator performance and workload. Participants were instructed to rely on the interface rather than operational experience to ensure consistent results. Findings showed that the interface improved aircraft adherence to altitude restrictions and reduced the number of clearances needed in more complex scenarios. By revealing the solution space, the ITSS encourages earlier planning and reduces the need for late-stage interventions. Future work will include professional controller evaluations, increased simulation fidelity, and extended training.

This book, Exercises, contains hundreds of exercises, including answers and worked examples, for studying and practicing the theory. Python scripts illustrate how to perform spectral signal analysis on a computer.

Startle and surprise are known to impair pilot performance during non-normal flight conditions. In the past years research has focused on developing strategies to help pilots mitigate startle and surprise responses to unexpected situations. However, no equivalent research exists for cabin crew. This study investigates the prevalence, impact on crew performance, and emotional consequences of startle and surprise among cabin crew, as well as the way in which it is currently trained. A survey was conducted among 348 European-based cabin crew to gather data on in-flight events that provoked startle, surprise, or both. Objective measures included stress ratings, perceived impact on performance, perceived emotional control, training exposure, and lasting anxiety. Qualitative data were also collected to assess crew responses and coping mechanisms. Out of 348 respondents, 79.3% reported having experienced startle, surprise or both. High stress levels were significantly correlated with perceived performance impairments and lasting anxiety. Current startle and surprise training improved perceived preparedness but did not reduce perceived stress or anxiety. Emotional control was deemed a key protective factor. Startle and surprise can cause operationally significant impact on cabin crew performance. Training that includes simple, effective emotional regulation strategies, such as those developed for pilots, is currently lacking and may help improve both immediate performance and long-term psychological resilience in the cabin environment, as may increased peer-support access.

Objective: We aim to investigate how pilots’ startle and surprise responses affect information-processing performance during simulated in-flight events. Background: Startle and surprise are distinct constructs, each with their own potential effects on pilot’s performance during unexpected in-flight events. Prior research suggests that startle may impair performance through stress-induced cognitive interference, whereas surprise may do so via cognitive demands associated with sensemaking. Thus, we hypothesized that both startle and surprise would negatively affect information-processing performance on a secondary auditory cognitive task. Method: Using a motion-based hexapod simulator and a twin-propeller aircraft model, 26 pilots each performed eight single-pilot flight scenarios, which were designed to elicit varying levels of startle and surprise responses. Linear mixed-effects models were employed to analyse the relationships between self-report startle and surprise with secondary task performance, while controlling for individual differences and differences between the scenarios. Results: The results revealed that higher startle was significantly associated with reduced information-processing speed. For surprise, no significant association was found. Conclusion: The findings suggest that, within the context of the tested scenarios, startle appeared to impose a more pronounced disruptive effect on pilots’ information-processing performance than surprise. Application: The study underscores the need for tailored interventions to enhance pilots’ resilience to startle and calls for further research on ecologically valid methods to induce surprise for research and training purposes.

Driving simulators are essential tools to guide automotive research and development. Their motion system requires a motion cueing algorithm (MCA) to keep the simulator motion platform within its physical boundaries, while simultaneously aiming to recreate the sensation of real vehicle motion. While traditional, filter-based approaches are still predominant, optimization-based MCAs have been at the center of MCA research for over a decade due to their ability to systematically improve motion cueing quality through explicit cost function design and constraints handling. However, despite their demonstrated advantages, these optimization-based methods have not yet achieved widespread adoption in driving simulation. This paper therefore provides a comprehensive review of optimization-based MCAs for driving simulation, categorizing and comparing algorithms, describing their key developments and core characteristics. The current limited real-time capability, lack of accurate evaluation methods, challenges in cost function design and its tuning, and the current lack of accurate future reference predictions are identified as key barriers to the practical deployment and widespread use of optimization-based MCAs. These theoretical and practical challenges are further reviewed, providing guidelines to advance the theory and application of optimization-based MCAs. Central in these advancements are a better understanding of which motions constitute a realistic motion experience, a framework allowing to compare the achieved motion fidelity of MCAs across papers, the design of the cost function focusing on human motion perception, and techniques for easing up the tuning process to swiftly reach high quality tunings for different simulators, scenarios, and use cases. We identify the need for improving the real-time capability, and providing high quality motion reference predictions using learning-based approaches on diverse datasets, along with techniques to handle existing uncertainties. Following these guidelines, new foundations for optimization-based algorithms in driving simulation can be achieved, which will significantly impact the research and development of automotive systems.

Engineering Signal Analysis is an introductory textbook on the analysis of signals in time and frequency domain. It discusses how to characterize, model, analyze, interpret and operate on signals in time and frequency. In the first part of the book the basic theoretical concepts are introduced in continuous time, covering the Fourier Series and Fourier Transform. The second part introduces discrete-time signals, addressing sampling and finite signal duration, and their implications on spectral analysis. The third part elaborates on spectral estimation, covering the basic periodogram and more advanced methods. The fourth part concludes the book with an introduction to linear system theory, and addresses in particular signal filtering.

This study was designed to validate the factor structure of the Startle and Surprise Inventories using multilevel confirmatory factor analysis in an ecologically valid flightdeck setting. The Startle and Surprise Inventories were developed to assess self-report startle and surprise to target stimuli. As their use expands in operational settings, construct validity should be further examined in contexts with ecological validity. 208 observations were collected from 26 professional pilots exposed to eight scenarios with varied levels of startle and surprise in a motion-based simulator. After each scenario, pilots completed the Startle and Surprise Inventories. A two-factor model, comprising the constructs Startle and Surprise, demonstrated superior and acceptable fit over a one-factor model. All items demonstrated significant factor loadings at both within- and between-scenario levels in the two-factor solution. McDonald’s ω ranged from ω = 0.88 to ω = 0.96 for the Startle Inventory, and ω = 0.77 to ω = 0.96 for the Surprise Inventory, indicating acceptable to excellent internal consistency. The findings offer empirical support for the construct validity and reliability of the Startle and Surprise Inventories in a highly ecologically-valid setting. The validated and reliable measures can inform evidence-based safety training protocols and interventions in aviation and other safety-critical domains.