Applied Ergonomics最新文献

This analysis examined systemic causes of Uncrewed Air Vehicle (UAV) accidents identifying operator, environmental, supervisory, and organisational factors through the use of the Human Factors Analysis and Classification System (HFACS). HFACS is a system-based analysis method for investigating the causal factors associated with accidents and incidents and has previously been used to reliably and systematically identify active and latent failures associated with both military and general aviation accidents. Whilst HFACS has previously been applied to UAV accidents, the last known application was conducted in 2014. Using reports retrieved from nine accident investigation organisations’ databases, causal factors were coded against unsafe acts, preconditions, and failures at the supervisory, organisational, and environmental levels. Causal factors were assessed on 77 medium or large UAV mishaps/accidents that occurred over a 12-year period up to 2024. 42 mishap reports were deemed to involve a human factor as a causal factor. A large proportion of the mishaps contained factors attributed to Decision Errors at level 1 (Unsafe Acts) which was found to be associated with both the Technological Environment and Adverse Mental State at level 2 (Pre-conditions). Causal factors were identified at each of the other 3 levels (Supervisory, Organisational and External) with a number of emergent associations between causal factors. These data provide support for the identification and development of interventions aimed at improving the safety of organisations and advice of regulators for Uncrewed Air Systems.

Integrating Artificial Intelligence (AI) and drone technology into bridge inspections offers numerous advantages, including increased efficiency and enhanced safety. However, it is essential to recognize that this integration changes the cognitive ergonomics of the inspection task. Gaining a deeper understanding of how humans process information and behave when collaborating with drones and AI systems is necessary for designing and implementing effective AI-assisted inspection drones. To further understand human-drone-AI intricate dynamics, an experiment was conducted in which participants’ biometric and behavioral data were collected during a simulated drone-enabled bridge inspection under two conditions: with an 80% accurate AI assistance and with no AI assistance. Results indicate that cognitive and behavioral factors, including vigilance, cognitive processing intensity, gaze patterns, and visual scanning efficiency can influence inspectors' performance respectively in either condition. This highlights the importance of designing inspection protocols, drones and AI systems based on a comprehensive understanding of the cognitive processes required in each condition to prevent cognitive overload and minimize errors. We also remark on the visual scanning and gaze patterns associated with a higher chance of missing critical information in each condition, insights that inspectors can use to enhance their inspection performance.

Industrial robots are increasingly commonplace, but research on prototypical accidents and injuries has been sparse, hindering evidence-based safety strategies. Using Severe Injury Reports (SIRs) from the U.S. Occupational Safety and Health Administration (OSHA), we identified 77 robot-related accidents from 2015-2022. Of these, 54 involved stationary robots, resulting in 66 injuries, mainly finger amputations and fractures to the head and torso. Mobile robots caused 23 accidents, leading to 27 injuries, mainly fractures to the legs and feet. A two-stage deductive–inductive thematic analysis was performed using text data from the final narratives in the reports to discover patterns in tasks, precipitating mechanisms, and contributing factors. Findings highlight the need for guards and collision avoidance systems that detect individual extremities. Post-contact strategies should focus on mitigating finger amputations. More structured and detailed narratives in the SIRs are needed.

Earplugs’ comfort is primarily evaluated through cost-effective laboratory evaluations, yet these evaluations often inadequately capture the multidimensional comfort aspects due to design limitations that do not replicate real-world conditions. This paper introduces a novel laboratory method for comprehensive assessment of the multidimensional comfort aspects of earplugs, combining questionnaire-based evaluations and objective perceptual tests within virtual industrial sound environments replicating in-situ noise exposure. Objective perceptual results confirm that the sound environment affect participants’ ability to detect alarms in a noisy environment and comprehend speech-in-noise while wearing earplugs. Subjective questionnaire results reveal that the earplugs family has an effect on the primary attributes of the acoustical, physical and functional comfort’s dimension. Participants reported the physical dimension as the most important factor they take into account when evaluating earplugs’ comfort. The functional dimension was considered the second most important factor by the participants, followed by the psychological dimension, and the acoustical dimension.

The operational environment of complex sociotechnical systems is inherently uncertain, demanding constant coordination restructuring to adapt to dynamic situational demands. However, coordination changes in the Human Factors and Ergonomics Field have primarily been studied using static methods, overlooking moment-by-moment adjustments. In the current study, we address coordination restructuring by using THEME, a digital analytical tool capable of visualising and exploring coordination restructuring from a multi-layered perspective. We examine restructuring in coordination patterns during NASA's Apollo 13 Mission, revealing significant shifts from stable, long-duration ‘coordination hubs' in routine operations to shorter-duration patterns during a crisis situation. Additionally, the results highlight the importance of flexible switching between reciprocal and one-directed coordination, along with enhanced role distribution. This study underscores how exploring temporality-sensitive phenomena like coordination through digital technologies such as THEME, advances our understanding of incident analysis and resilient performance within complex systems.

Limb amputation can lead to significant functional challenges in daily activities, prompting amputees to use prosthetic devices (PDs). However, the cognitive demands of PDs and usability issues have resulted in user rejections. This study aimed to create a Human Performance Model for Upper-Limb Prosthetic Devices (HPM-UP). The model used formulations of learnability, error rate, memory load, efficiency, and satisfaction to assess usability. The model was validated in an experiment with 30 healthy participants using a bypass prosthetic device. Findings indicated that the HPM-UP successfully predicted the usability of prosthetic devices, aligning with human subject data. This research proposes a quantitative approach to predict upper limb prosthetic device usability by quantifying each dimension and computationally connecting them. The model, available on Github and executable with Rstudio, could enable clinicians to assess and analyze the human performance of various commercial prostheses, aiding in recommending optimal devices for patients.

First responder professionals are at high risk for work-related injuries (e.g., extreme temperatures, chemical and biological threats); boots are essential to ensure body protection since they have full contact with the ground in all scenarios. A substantial body of work has investigated the necessity of improvements in protective boots, but there is limited research conducted on boots with fit-adjustable fasteners for secure and adjustable fit within this context. Thus, this study explored the areas for improvement in boot design for the development of form-fitting and yet comfortable boots focusing on two different boot designs, prototype all-hazards tactical boots (lace-up) and rubber boots (slip-on). Findings indicated that the boot design should address participants’ concerns with the material choices of boots, specifically with bulkiness, weight, and flexibility. Our findings provide insights into boot material and design choices to improve protective boots for first responders.

In a previous scoping review, eight categories of interventions in individual work practice were defined. The aim of the present study is to evaluate the relevance and completeness of these eight categories and to increase the clarity of the nomenclature and definitions of each category. An international expert consultation has been carried out for this purpose. Thirty-eight experts from 13 countries participated. Data collection was conducted using a survey design comprising structured questions. Consensus was reached if 75% of the experts answered ‘Strongly agree’ or ‘Agree’ on a 5-point Likert scale. For the topic ‘Relevance’, there was consensus for six of the eight categories (range 78%–86%), the exceptions were the categories: ‘Exercising’ (72%) and ‘Professional manners' (64%). With regard to the topic ‘Nomenclature’, consensus was reached for six categories and for the topic ‘Definition’ this was five categories. The present definitions have been improved based on the expert recommendations. With respect to the topic ‘Completeness’: although a limited number of suggestions were given, this did not lead to one or more categories being added to the existing eight categories. The final ‘Nomenclature’ for the categories is: ‘Variation’, ‘Professional behaviour’, ‘Motoric skills’, ‘Vocational working techniques’, ‘Physical workplace’, ‘Physical training’, ‘Assistive devices and tools’ and ‘Task content and task organisation’. This expert consultation has provided a solid basis for endorsing the categorisation of interventions in IWP and is an important step in building a framework to develop and evaluate interventions in IWP.

Fit and accommodation are critical design goals for a body armor system to maximize Soldiers’ protection, comfort, mobility, and performance. The aim of this study is to assess fit and accommodation of body armor plates for the US Army. A virtual fit assessment technique, developed, validated, and deployed by NASA for spacesuit design, was adopted for this work. Specifically, 3D manikins of the Soldier population were overlaid virtually with geometrically similar surrogates of the armor plates. Trained subject matter experts with the US Army and NASA manually assessed the fit of the armor plates to manikins using a computer visualization tool and selected the appropriate plate size and position. A prediction model was built from the assessment data to predict the plate size from an arbitrary body shape and the resultant patterns of body-to-plate contact were quantified. The outcome indicated a unique trend of the plate sizes covarying with anthropometry. More pronouncedly, when the overlap between the body tissue and armor plate was quantified, female Soldiers are likely to experience a 25 times larger body-to-plate contact volume and 6.5 times larger contact depth than males on average, due to sex-based anthropometric differences. Overall, the prediction model and contact patterns provided key metrics for virtual body armor fit assessments, of which the locations, patterns, and magnitudes can help to improve sizing and fit of body armor systems, as previously demonstrated for NASA spacesuit design.

Augmented reality (AR) environments are emerging as prominent user interfaces and gathering significant attention. However, the associated physical strain on the users presents a considerable challenge. Within this background, this study explores the impact of movement distance (MD) and target-to-user distance (TTU) on the physical load during drag-and-drop (DND) tasks in an AR environment. To address this objective, a user experiment was conducted utilizing a 5× 5 within-subject design with MD (16, 32, 48, 64, and 80 cm) and TTU (40, 80, 120, 160, and 200 cm) as the variables. Physical load was assessed using normalized electromyography (NEMG) (%MVC) indicators of the upper extremity muscles and the physical item of NASA-Task load index (TLX). The results revealed significant variations in the physical load based on MD and TTU. Specifically, both the NEMG and subjective physical workload values increased with increasing MD. Moreover, NEMG increased with decreasing TTU, whereas the subjective physical workload scores increased with increasing TTU. Interaction effects of MD and TTU on NEMG were also significantly observed. These findings suggest that considering the MD and TTU when developing content for interacting with AR objects in AR environments could potentially alleviate user load.