Human Factors最新文献

ObjectiveThis work aims to estimate the portion of electric vehicle (EV) users who exhibit procrastination-like behavior, almost equivalent to an "empty" battery, before they decide to charge their vehicles.BackgroundThere is a human tendency to procrastinate when a deadline approaches. Human behavior in the presence of deadlines has been studied in different fields to evaluate individuals' performance or organizational efficiency and effectiveness. However, this phenomenon has not been investigated among EV users.MethodThis study explores users' procrastination-like behavior among 69 Rhode Island public charging stations' data representing 70,611 charging events. The Deadline Rush Model is incorporated to model frequent users' charging profiles. To conduct a robust estimation, the Bayesian Mixture Model is implemented.ResultsWith the selection of an informative prior, the Bayesian Mixture Model estimated that almost one-third of frequent users procrastinate charging.ConclusionThe majority of procrastination-like users have small battery sizes. Although procrastination-like users need to charge when they arrive at a location, that might not necessarily be true for a plug-in hybrid; thus, systematically, they can clog the system for other users whose needs are more pressing. Understanding unique and unexplored charging behaviors among EV users is beneficial to EV infrastructure stakeholders in reducing the adoption threshold by providing a reliable and ubiquitous charging network.ApplicationThe findings identify a different kind of demand on the EV infrastructure than previously modeled and can directly influence future decision-making criteria in terms of planning to optimize to accommodate EV drivers with different charging behaviors.

ObjectiveAs the world increasingly adopts renewable and sustainable energy systems, transitionary solutions include nuclear power, which currently provides 20% of the United States' electricity and is the largest single source of carbon-free electricity generation. Advanced reactors are a critical component of a carbon-free mixed energy portfolio that require careful design of first-of-a-kind control rooms.BackgroundThe application of Human Factors Engineering (HFE) is essential for scientific and iterative testing of novel human-system interface (HSI) concepts to ensure effective, efficient, and safe plant operations. Microworlds are simulators that use simplified physics models and control systems to distill nuclear power operations into essential functions.MethodHFE scientists used the Rancor Microworld Simulator to obtain preference and performance metrics for novel and traditional static HSI design styles. Participants comprised advanced reactor company employees and nuclear industry consultants. A mixture of quantitative and qualitative data was captured.ResultsThere was a preference for the basic graphical style that included high contrast and traditional color scheme elements. No single HSI design outperformed the others, and the participants did not perform better using their preferred HSI style.ConclusionThis experiment is the first in a series of HFE testing for HSIs in advanced reactor control room development. Clear user preferences emerged for elements within static displays. The cutting-edge neumorphic style was the least preferred. Future directions include tests of dynamic displays.ApplicationHFE is used in evaluating and designing HSI devices that will improve the efficiency and safety of advanced nuclear power operations.

ObjectiveWe conducted a literature review investigating the validity of eye tracking metrics appropriately representing trust in automation.BackgroundAs researchers grow interested in measuring trust in automation, there has been a need to find a reliable and accurate measurement tool. Many articles have measured automation trust using eye tracking, assuming that as trust increases, visual attention from eye tracking metrics decreases. Eye tracking is an attractive potential measure for its nonintrusive and objective nature.MethodIn this systematic literature review, we looked at studies that have tested the relationship between eye tracking and trust to determine its validity and reliability.ResultsAcross 22 articles that investigated the relationship between trust and eye tracking, only about half found a negative significant relationship, whereas the other half found no relationship at all.ConclusionThe relationship between automation trust and eye tracking is inconsistent and unreliable. A wide variety of trust and eye tracking metrics were used, but only about half of the papers found any kind of relationship. The relationship did not appear robust enough to prevail when different eye tracking and trust metrics were applied in various study designs.ApplicationAn effective eye tracking-trust relationship would be useful in various applications (e.g., autonomous driving). Further, this relationship is crucial when there is a clear distinction between attention allocated to automated components of a system (e.g., car display) and unrelated displays to allow for an easy separation of a location associated with high trust versus low trust.

ObjectiveThe objective is to develop a privacy-preserving federated learning framework and evaluate its efficacy for two specific human factors applications: classifying mental stress levels in human-robot collaboration and recognizing human activities during manual material handling.BackgroundMachine learning, as a transformative tool, has reshaped the landscape of human factors and ergonomics research. Nevertheless, traditional centralized machine learning methods often encounter critical data privacy issues, especially when dealing with sensitive human data. This study addresses these concerns by implementing a federated learning approach.MethodsClassifiers were constructed using both centralized and federated approaches, with machine learning techniques customized for each application. For mental stress classification, we utilized feature-based machine learning techniques, such as support vector machine. For human activity recognition, we deployed a deep neural network combining long short-term memory and convolutional neural network layers. Comparative analysis in terms of precision, recall, and F1-score was conducted to evaluate the performance of the federated and centralized models.ResultsThe results demonstrate that federated learning not only offers comparable accuracy to centralized methods but also ensures the protection of sensitive data. The performance differences were minimal across both applications, with discrepancies remaining under 2.7%.ConclusionFederated learning proves to be a promising alternative to traditional machine learning models, offering comparable accuracy while significantly enhancing data privacy.ApplicationThe study's outcomes are particularly relevant for advancing privacy-preserving methodologies in fields involving sensitive human-subject data.

This special section of Human Factors includes papers on the role of human factors concepts in sustainable design. Human factors and ergonomics (HF/E) play a critical role in the design for usability and reliability of increasingly complex systems. Researchers were invited to submit manuscripts that address HF/E issues in the design and adoption of renewable and sustainable energy systems and devices.

ObjectiveThe effects of three prototypical designs of energy consumption displays on energy-specific situation awareness were examined.BackgroundEnergy efficiency is crucial for the sustainability of technical systems. However, without accurate situation awareness of energy dynamics (energy dynamics awareness, EDA) it can be challenging for humans to optimize the use of energy resources of electric vehicles (EVs) through their behavior.MethodWe examined three prototypical energy display designs that varied by their informational value to support EDA. Furthermore, we investigated the differential effects on EDA measured by (1) a newly constructed scale (experienced EDA), (2) estimating energy consumption, and (3) identifying efficient trips in an online experiment. Participants (N = 82) watched standardized driving scenes (videos) of EV trips presenting the energy displays.ResultsWe found a strong effect of display type on experienced EDA, with the trace display being the most supportive. The EDA scale showed excellent internal consistency. The consumption estimation and efficient trip identification indicators were not affected by the display type.ConclusionThe study indicates that experienced EDA is immediately affected by displays with higher information value, but performance might need more time and training. More research is needed to investigate the cognitive processes related to EDA and to examine how distinct display elements enhance EDA.ApplicationResults from this research can be used as guidance for the design of energy displays, especially in EVs. The EDA scale can be used as an evaluation measure in the human-centered design process of energy displays.

ObjectiveTo investigate the effect of visual degradation on simulated flight performance, perceived stress, and perceived task difficulty.BackgroundEstablishing visual standards for pilots is crucial, although it may limit the pool of eligible candidates and impact pilot retention. Despite this, there is limited understanding regarding the influence of vision on pilot performance.MethodTwenty participants (0-300 flight hours) completed a flight simulation task using the ALSIM AL250 in two experiments. Distance static visual acuity (VA) ranged from 6/6 (20/20) to 6/60, with scenarios including no vision. Experiment 1 (n = 10) tested landing performance for 6 VA conditions, while experiment 2 (n = 10) involved a more difficult circuit task (traffic pattern) with 8 VA conditions. Participants completed stress and difficulty questionnaires between trials. Flight performance variables assessed were vertical speed, altitude, attitude, pitch, and roll.ResultsIn both flight simulation experiments, vision degradation did not affect novice pilots' landing performance, but complete loss of vision led to loss of control. Participants in experiment 1 experienced stress at lower perturbation level than in experiment 2.ConclusionVision degradation up to 6/60 had no discernible impact on novice pilots' simulated approach to landing or flight circuit and landing. Total vision loss led to loss of aircraft control. Perceived stress and difficulty increased with reduced vision.ApplicationThis research opens the door to reexamine the visual standards for pilots and serve as a simple tool to manipulate perceived stress and difficulty in operational tasks.