Browse Topic: Human factors
Passive fatigue can cause accidents with automated and regular vehicles. A proof-of-concept prototype [made with light-emitting diode (LED) matrices and white LED (WLED)] and a preliminary comparative usability test (N = 7) are used to study whether the active manipulation of simulated weather cues can be a potential countermeasure to passive fatigue. Participants rated system suitability, system impression, and their fatigue level similarly when they viewed a weather windshield heads-up display (HUD) versus a speedometer windshield HUD [no significant differences found and relatively small 95% confidence interval (CI) ranges around 0]. Qualitative analysis of interviews found that participants saw the potential value of the weather display and that display placement, dynamic graphics, and user activation were commonly mentioned themes. These results suggest the concept is theoretically possible, though further work is needed to prove the concept in practice.
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence modeling, model training, and interpretability analysis. The study demonstrates how this pipeline uncovers hidden relationships among operational errors, environmental influences, maintenance actions, design considerations, and crew behavior. The findings show that the integrated approach improves the accuracy and stability of risk prediction and highlights specific human factors patterns that consistently contribute to elevated risk levels. These insights support targeted mitigation strategies, inform design improvements, and help prioritize safety interventions. The work concludes that a combined human factors and predictive modeling framework enhances the ability of organizations to identify vulnerabilities earlier, allocate resources more effectively, and strengthen system resilience. This approach is adaptable to diverse aviation contexts and offers a practical path for transforming human factors data into actionable safety intelligence.
This SAE Aerospace Recommended Practice (ARP) provides criteria for the design, installation, operation, and training aspects of head-up display (HUD) systems in transport category aircraft, with emphasis on pilot interface and operational requirements. The recommendations apply to permanently installed (including stowable) HUDs that display primary flight information, including those integrating enhanced flight vision system (EFVS) imagery. The intent is to ensure HUDs are designed and used in a manner that improves pilot situational awareness and flight technical performance across all phases of flight, up to and including low-visibility operations. While technical design standards (optical performance, hardware specs, etc.) are defined in documents like ARP5288 and AS8055, this document focuses on pilot usage considerations and human factors. HUD systems addressed here are typically designed to support a fail-passive operational concept applicable to Category III instrument approach operations, where approved, though many recommendations also apply to HUD usage for Category I and II operations and other phases of flight. Devices such as head-worn displays are not specifically covered, though future provisions may consider “through-display” wearable systems as technology matures.
Rotorcraft pilots operating in degraded visual environments encounter significant challenges during hover flight, where the absence of critical visual cues increases the risk of spatial disorientation. At low altitudes and in obstacle-rich environments, even minor losses in situational awareness can have severe consequences. Understanding the visual cues that support stable hover in good visual environments, and how their absence impacts performance and cognitive workload, is essential for mitigating these risks. This study examined key human factors in hover flight, focusing on the role of peripheral vision and microtextures in supporting pilot performance. It evaluated whether naturally relied-upon visual cues in good visual environment conditions can be artificially replicated to restore visual dominance in simulated degraded visual environments. Analysis included flight performance metrics, control inputs, physiological workload indicators, subjective assessments, and pilot feedback. The findings contribute to improved understanding of visual cueing and pilot adaptation in degraded conditions.
This paper presents an experimental investigation of ship airwake-rotor interaction under cruise-only and longitudinal gust conditions (cruise + gust). A model-scale NATO Generic Destroyer and rotorcraft were tested using time-resolved stereoscopic particle image velocimetry, a six-axis force/torque load cell, and flush pressure sensors. Flow structures, pressure distributions, and spectral energy within the pilot workload-relevant frequency bands were analyzed. High-pressure regions on the ship deck surface show the interactions between the ship recirculation region and rotor ground effects from downwash. The reduced forward velocity within the airwake leads to decreased thrust and a nose-down pitching moment across the ship deck. For high-disk-loading rotorcraft, the rotor ground effects are less important than the ship airwake effects. The power spectral densities of CT, CMx, and CMy decrease toward higher frequencies, while the PSDs of CFx and CFy retain comparatively higher energy at the upper end of the full-scale pilot workload frequency band. A maximum increase of 54.3% in the CMy pilot workload factor in the cruise + gust conditions is observed at Ldeck, which denotes a significant rise in the demand of pilot inputs near the ship stern. The overall pilot workload factor in the cruise + gust case increased by 35.6% compared to cruise-only at 1.5Ldeck, and decreases into the deck. Overall, gust-driven airwake dynamics intensify rotor loading and increase pilot workload demands during shipboard helicopter operations.
In area of modern manufacturing, ensuring product quality and minimizing defects are utmost important for maintaining competitive advantage and customer satisfaction. This paper presents an innovative approach to detect defect by leveraging Artificial Intelligence (AI) models trained using Computer-Aided Design (CAD) data. Traditional defect detection methods often rely on physical inspection, which can be time-consuming and prone to human error. The conventional method of developing an AI model requires a physical part data, By utilizing CAD data, the time to develop an AI model and implementing it to production line station can be saved drastically. This approach involves the use of AI algorithms trained on CAD models to detect and classify defects in real-time. The field trial results demonstrate the effectiveness of this approach in various industrial applications, highlighting its potential to revolutionize defect detection in manufacturing.
Accurate defect quantification is crucial for ensuring the serviceability of aircraft engine parts. Traditional inspection methods, such as profile projectors and replicating compounds, suffer from inconsistencies, operator dependency, and ergonomic challenges. To address these limitations, the 4D InSpec® handheld 3D scanner was introduced as an advanced solution for defect measurement and analysis. This article evaluates the effectiveness of the 4D InSpec scanner through multiple statistical methods, including Gage Repeatability and Reproducibility (Gage R&R), Isoplot®, Youden plots, and Bland–Altman plots. A new concept of Probability of accurate Measurement (PoaM)© was introduced to capture the accuracy of the defect quantification based on their size. The results demonstrate a significant reduction in measurement variability, with Gage R&R improving from 39.9% (profile projector) to 8.5% (3D scanner), thus meeting the AS13100 Aerospace Quality Standard. Additionally, the 4D InSpec scanner improved detection accuracy, provided automated defect quantification, and eliminated the need for time-consuming replication processes. Beyond performance improvements, the adoption of the 4D InSpec scanner led to a 75% reduction in direct labor time, significant cost savings, and the elimination of ergonomic risks and human error associated with traditional inspection methods, and enhanced defect reporting and data collection. The article closes with implementation requirements and areas for future improvement.
The assessment of collision risks is crucial for effective risk control and scientific management of maritime safety. To prevent maritime transportation accidents, an accident causation model has been proposed to analyze risks in maritime transportation systems. The 24-model further analyzes the impact pathways of accident factors in the accident chain and calculates the fit of HOF-related factors. Using Bayesian Networks as a foundation and the 24-model as a tool, a Bayesian Network model for collision risk is constructed by identifying risk factors and determining their correlations, utilizing accident data from Chinese maritime authorities. Utilizing a Bayesian Network to construct a ship collision risk model that couples HOF and calculates conditional probabilities of relevant node occurrences. To explore the coupled relationships between nodes in a network, this study employs the N-K model to construct a safety risk coupling model for ship collision accidents, calculating risk values for different coupling types within the model. Case analysis shows that accidents result from dynamic interaction and linear combination of risk factors. The analysis of experimental results indicates that various accident factors contribute differently to overall maritime risk. Human factors are the direct cause of maritime ship collision accidents. From the perspective of coupled risk, organizational factors, as root influences, are crucial aspects that bridge resource management needs to focus on. The application of this model provides maritime personnel with a novel approach to mitigate the risk of maritime collisions.
Not a traditional university lab, Harvard University’s Move Lab employs professional engineers, product developers, and academics who work across disciplines to bring research innovations to market. The lab is focused on human performance enhancement to protect people’s physical ability to guard against injury, extend their abilities beyond the limits of advancing age, and restore them to people who have lost them. They have developed wearable solutions that support functional movements and allow impaired individuals to more easily interact with their environment.
The design, development, and optimization of modern suspension systems is a complex process that encompasses several different engineering domains and disciplines such as vehicle dynamics simulation, tire data analysis, 1D lap-time simulation, 3D CAD design and structural analysis including full 3D collision detection. Typically, overall vehicle design and suspension development are carried out in multiple iterative design loops by several human specialists from diverse engineering departments. Fully automating this iterative design process can minimize manual effort, eliminate routine tasks and human errors, and significantly reduce design time. This desired level of automation can be achieved through digital modeling, automated model generation, and simulation using graph-based design languages and an associated language compiler for translation and execution. Graph-based design languages ensure the digital consistency of data, the digital continuity of processes, and the digital interoperability of all engineering software tools along the product life cycle (PLC). In this context, they are used to automate the design and development of a suspension system for a Formula student racing car. The automated design consists of an inner design loop for simulating suspension system properties, including a 1D lap-time simulation, and an outer loop for the 3D shape optimization of the modeled anti-roll bar geometry, including 3D collision detection. These nested loops are executed automatically, optimizing the vehicle's kinematics through a particle multi-swarm optimization algorithm. This generic design automation approach for suspension systems leads to improved design quality in significantly less time and at a lower cost.
Pilot workload assessment has been a keen area of research for many years and has key applicability in flight testing. This paper outlines the development of a novel workload rating scale and index, the Comeau-Duggan Pilot Workload Index, which bridges gaps, such as causal factor identification, between some of the most widely used rating scales in flight test. The conceptualization and evolution of this index has been a multi-year and multi-nation research effort that has built upon the foundation and fundamental principles that underpin current widely accepted workload rating scales used in Human Factors and Handling Qualities engineering. The pilot workload index facilitates a rigorous and robust methodology for identifying the factors contributing to a given flying task, quantifying their impact through a structured suffix flowchart approach. It can provide, for example, a quantifiable link between pilot workload and the operational use of the aircraft, and therefore could inform aircraft and system design, as well as tactics and procedural development. It was developed through flight trials conducted at the National Research Council of Canada and flight simulator trials conducted at the University of Liverpool.
This paper demonstrates the training, optimisation, and predictive capabilities of Machine Learning (ML) for helicopter-ship certification. The work focuses on the development of a Linear Discriminant Analysis (LDA) model, trained specifically on pilot control activity data recorded during the hover phase of a recovery to a ship, to determine an operational boundary driven by pilot workload. The certification process currently relies heavily on embarked trials and the subjective workload assessment of test pilots. Modelling and Simulation (M&S), however, offers a potentially more efficient approach to addressing the high costs, resource-intensive nature, and inherent dangers associated with traditional clearance methods. By providing a relatively large amount of data for analysis, this approach creates an opportunity to bridge the gap between subjective and objective measures, enabling the prediction of workload limitations. An LDA model was trained using cross-validation on pilot control activity data and optimised through the inclusion of a penalty factor to reduce overfitting. Throughout the training process, the model demonstrated good performance, effectively distinguishing between high and low workload conditions based on pilot control activity data. When tested on unseen data, the model accurately predicted the Ship-Helicopter Operating Limit (SHOL) boundary for most cases. These results support the application of ML in the helicopter-ship certification process and demonstrate the model's ability to identify correlations within high-dimensional datasets, offering a more data-driven and objective approach to determining workload and clearance boundaries.
As part of a human factors research project aimed at optimizing technical documentation used in helicopter maintenance with multimedia elements, we compared different instruction formats to observe their effects on the performance of an assembly task. This task offers us the opportunity to test procedures that call for similar actions as a maintenance task (e.g., localization, action sequencing, assembly). Static (i.e., image and image with text) and dynamic instruction formats (i.e., video, video with text and video with audio) were compared to determine if dynamic formats allowed a better motor performance of the task for assembly reaction time (time needed to complete the assembly) and accuracy. We were also interested in how the use of the text instructions interacted with both visual dynamic and static instructions. Reaction times were recorded and measured with eye tracking data. Subjective data was collected in questionnaires during and after the experiment. Results showed significant differences in the time spent on the instructions and the time spent on the assembly, depending on the format of instructions. Overall, assembly time is shorter with video instruction formats, but videos took longer to be consulted than static formats. Results also showed a difference in the number of actions required to do the assembly. Videos facilitated the right path of action sequence in comparison with static formats. With the analysis of both subjective and objective data, the results give us a better idea of the advantages and drawbacks of using dynamic formats in technical documentation.
Letter from the Guest Editors
The author’s life work in acoustics and sound quality, continuous over more than 40 years, has followed a number of branches all involving measurement technologies and their evolution. The illustrated discussion begins 60 years ago in 1965 at Arizona State University in its Frank Lloyd Wright-designed Gammage Auditorium, and moves to the Research and Development Division of Kimball International, Inc. (Jasper, Indiana) in 1976 with piano research using a Federal Scientific Ubiquitous analog real-time FFT analyzer and Chladni-plate-mode studies with fine sand and high-speed photography of sound board modes. It continues at Jaffe Acoustics, Inc., a concert-hall-specializing consultancy in Norwalk, CT, with early-reflection plotting using a parabolic microphone on an altazimuth angular-readout mounting and either photographing oscillograms, or running a high-speed paper chart printer, assembling “wheel plots” incremented every 10 degrees in azimuth and altitude to map reflection patterns. Involvement with binaural technique began for me in 1986 and led me into the automotive industry, whose SQ evolution and that of HEAD acoustics will be outlined along with an earlier side-branch courtesy of James Shedlowsky (GM retired): a photo-archive of GM pseudo-binaural and binaural techniques and jury evaluations starting in 1952 which has been presented in an earlier Noise and Vibration Conference’s Science Fair.
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured, incorrectly formatted or incorrectly collected. This paper discusses a possible use case of leveraging automation to help meet these needs. By automating data cleaning and analysis, companies obtain insights more quickly, reduce the need for manual-based data intervention, and minimize the risk of human error. Beyond streamlining data analysis, automation enables real-time interventions, such as cleaning data as it comes in (event-driven automation) and providing continuous system monitoring. This paper explores the significant role of automation playbooks in enhancing these processes. Playbooks facilitate the creation and management of automation flows by treating scripts and tasks as modular blocks that can be easily arranged and modified to suit different environments and setups. This modular approach allows for greater flexibility and a "hands-off" automation model, where processes can be deployed on a scheduled basis with minimal manual oversight. In essence, automation can play a critical role in manufacturing's needs to reliably make sense of their data, and the use of playbooks further optimizes these benefits by simplifying the management and execution of automated tasks.
Items per page:
50
1 – 50 of 1341