Results for “human systems performance” — Page 21

NASA NTRS2015

Human Factors Assessment of Disturbances to Scheduled Performance-Based Navigation Arrival Operations

The introduction of Performance-Based Navigation (PBN) specifications to air traffic management has resulted in many benefits during nominal operations, including shorter flight paths, reduced fuel costs, and improved terminal area arrival rates. However, these benefits become less noticeable during off-nominal operations where aircraft are routinely interrupted from staying on PBN procedures due to disturbances such as missed approaches. This human-in-the-loop (HITL) study used multiple types of disturbance events to perturb the arrival schedule. Perturbed schedules were managed with different types of schedule adjustments, including a condition with no adjustments. The study collected data on a host of dependent variables, including human factors measures on controller workload and system performance measures such as schedule nonconformance (nc). Initial analyses showed strong correlations between aggregated controller workload and aggregated nc, as well as benefits of both automatic and manual schedule adjustments for increasing system performance, such as reduced PBN procedure interruptions. The goal of this paper is to further test these initial findings. The results indicated that an increase in schedule nonconformance correlated with an increase in controller workload at specific time intervals, and automated schedule adjustments consistently reduced controller workload associated with nonconformance.

Human factors↗

NASA NTRSDate not supplied

Modeling of Gateway Environmental Control and Life Support Systems as A Means to Investigate the Subsystem and Integrated Architecture Performance

As Human spaceflight evolves and develops, the technology the crew relies on for life support must become more advanced than at any point in NASA’s history. Nowhere is this more apparent than the Gateway, where lessons learned from Mercury to ISS are being applied in the design and production of the life support systems. Some of these technological improvements are proven in flight configuration, or have a heritage of proven flight hardware, but many are of a lower technology readiness level. Due to the unpredictable nature of the metabolic byproducts (CO 2 , H 2 O, and heat), even the proven technologies can fail to meet requirements for crew safety. Detailed modeling of individual components excels in proving component level requirements are met, but fails to verify system or architecture level requirements. This paper expounds upon an effort to take a number of detailed component level models of the Gateway ECLSS and integrate them into a larger architecture model known as the Gateway Integrated ECLSS Model (GIEM). The GIEM is then used to study how the subsystems work synergistically to meet environmental requirements as well as investigate how changes at the component level effect the Gateway stack as a whole.

Modeling↗

NASA NTRS1974

A guide for performing system safety analysis

A general guide is presented for performing system safety analyses of hardware, software, operations and human elements of an aerospace program. The guide describes a progression of activities that can be effectively applied to identify hazards to personnel and equipment during all periods of system development. The general process of performing safety analyses is described; setting forth in a logical order the information and data requirements, the analytical steps, and the results. These analyses are the technical basis of a system safety program. Although the guidance established by this document cannot replace human experience and judgement, it does provide a methodical approach to the identification of hazards and evaluation of risks to the system.

Brush, J. M.↗

NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗

NASA NTRSDate not supplied

International Space Station as a Testbed for Exploration Environmental Control and Life Support Systems - 2021 Status

Human exploration missions beyond low earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will provide an updated status on the testbed development including hardware and ISS vehicle integration progress to date as well as future plans for efforts to design, select, build, test and fly Exploration ECLSS on the ISS.

Laura A Shaw↗

NASA NTRSDate not supplied

International Space Station as a Testbed for Exploration Environmental Control and Life Support Systems - 2022 Status

Human exploration missions beyond low earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will provide an updated status on the testbed development including hardware and ISS vehicle integration progress to date as well as future plans for efforts to design, select, build, test and fly Exploration ECLSS on the ISS.

ECLSS↗

NASA NTRSDate not supplied

International Space Station as a Testbed for Exploration Environmental Control and Life Support Systems - 2023 Status

Human exploration missions beyond low earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will provide an updated status on the testbed development including hardware and ISS vehicle integration progress to date as well as future plans for efforts to design, select, build, test and fly Exploration ECLSS on the ISS.

ECLSS↗

NASA NTRSDate not supplied

International Space Station (ISS) as A Testbed for Exploration Environmental Control and Life Support Systems (ECLSS)- 2023 Status

Human exploration missions beyond low earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will provide an updated status on the testbed development including hardware and ISS vehicle integration progress to date as well as future plans for efforts to design, select, build, test and fly Exploration ECLSS on the ISS.

ECLSS↗

NASA NTRS2024

International Space Station as a Testbed for Exploration Environmental Control and Life Support Systems - 2024 Status

Human exploration missions beyond low earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will provide an updated status on the testbed development including hardware and ISS vehicle integration progress to date as well as future plans for efforts to design, select, build, test and fly Exploration ECLSS on the ISS.

ECLSS↗

NASA NTRSDate not supplied

International Space Station as a Testbed for Exploration Environmental Control and Life Support Systems – 2024 Status

Human exploration missions beyond low Earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will provide an updated status on the testbed development, including hardware and ISS vehicle integration progress to date, as well as future plans for efforts to design, select, build, test, and fly Exploration ECLSS on the ISS.

ECLSS↗

NASA NTRS2024

International Space Station (ISS) as a Testbed For Exploration ECLSS – 2024 Status

Human exploration missions beyond low Earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions.

ECLSS↗

NASA NTRS2016

Capability for Integrated Systems Risk-Reduction Analysis

NASA's Human Research Program (HRP) is working to increase the likelihoods of human health and performance success during long-duration missions, and subsequent crew long-term health. To achieve these goals, there is a need to develop an integrated understanding of how the complex human physiological-socio-technical mission system behaves in spaceflight. This understanding will allow HRP to provide cross-disciplinary spaceflight countermeasures while minimizing resources such as mass, power, and volume. This understanding will also allow development of tools to assess the state of and enhance the resilience of individual crewmembers, teams, and the integrated mission system. We will discuss a set of risk-reduction questions that has been identified to guide the systems approach necessary to meet these needs. In addition, a framework of factors influencing human health and performance in space, called the Contributing Factor Map (CFM), is being applied as the backbone for incorporating information addressing these questions from sources throughout HRP. Using the common language of the CFM, information from sources such as the Human System Risk Board summaries, Integrated Research Plan, and HRP-funded publications has been combined and visualized in ways that allow insight into cross-disciplinary interconnections in a systematic, standardized fashion. We will show examples of these visualizations. We will also discuss applications of the resulting analysis capability that can inform science portfolio decisions, such as areas in which cross-disciplinary solicitations or countermeasure development will potentially be fruitful.

Mindock, J.↗

NASA NTRS1973

Identification of human operator performance models utilizing time series analysis

The results of an effort performed by Sperry Systems Management Division for AMRL in applying time series analysis as a tool for modeling the human operator are presented. This technique is utilized for determining the variation of the human transfer function under various levels of stress. The human operator's model is determined based on actual input and output data from a tracking experiment.

Holden, F. M.↗

NASA NTRS1989

Implementation and design of a teleoperation system based on a VMEBUS/68020 pipelined architecture

A pipelined control design and architecture for a force-feedback teleoperation system that is being implemented at the Jet Propulsion Laboratory and which will be integrated with the autonomous portion of the testbed to achieve share control is described. At the local site, the operator sees real-time force/torque displays and moves two 6-degree of freedom (dof) force-reflecting hand-controllers as his hands feel the contact force/torques generated at the remote site where the robots interact with the environment. He also uses a graphical user menu to monitor robot states and specify system options. The teleoperation software is written in the C language and runs on MC68020-based processor boards in the VME chassis, which utilizes a real-time operating system; the hardware is configured to realize a four-stage pipeline configuration. The environment is very flexible, such that the system can easily be configured as a stand-alone facility for performing independent research in human factors, force control, and time-delayed systems.

Lee, Thomas S.↗

NASA NTRS2001

Use of Human Modeling Simulation Software in the Task Analysis of the Environmental Control and Life Support System Component Installation Procedures

Virtual reality and simulation applications are becoming widespread in human task analysis. These programs have many benefits for the Human Factors Engineering field. Not only do creating and using virtual environments for human engineering analyses save money and time, this approach also promotes user experimentation and provides increased quality of analyses. This paper explains the human engineering task analysis performed on the Environmental Control and Life Support System (ECLSS) space station rack and its Distillation Assembly (DA) subsystem using EAI's human modeling simulation software, Jack. When installed on the International Space Station (ISS), ECLSS will provide the life and environment support needed to adequately sustain crew life. The DA is an Orbital Replaceable Unit (ORU) that provides means of wastewater (primarily urine from flight crew and experimental animals) reclamation. Jack was used to create a model of the weightless environment of the ISS Node 3, where the ECLSS is housed. Computer aided drawings of the ECLSS rack and DA system were also brought into the environment. Anthropometric models of a 95th percentile male and 5th percentile female were used to examine the human interfaces encountered during various ECLSS and DA tasks. The results of the task analyses were used in suggesting modifications to hardware and crew task procedures to improve accessibility, conserve crew time, and add convenience for the crew. This paper will address some of those suggested modifications and the method of presenting final analyses for requirements verification.

Estes, Samantha↗

DOE OSTI2026

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗

NASA NTRS1988

Engineering data compendium. Human perception and performance, volume 3

The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design of military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by system designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is Volume 3, containing sections on Human Language Processing, Operator Motion Control, Effects of Environmental Stressors, Display Interfaces, and Control Interfaces (Real/Virtual).

Boff, Kenneth R.↗

NASA NTRS2010

Integration of MSFC Usability Lab with Usability Testing

As part of the Stage Analysis Branch, human factors engineering plays an important role in relating humans to the systems of hardware and structure designs of the new launch vehicle. While many branches are involved in the technical aspects of creating a launch vehicle, human factors connects humans to the scientific systems with the goal of improving operational performance and safety while reducing operational error and damage to the hardware. Human factors engineers use physical and computerized models to visualize possible areas for improvements to ensure human accessibility to components requiring maintenance and that the necessary maintenance activities can be accomplished with minimal risks to human and hardware. Many methods of testing are used to fulfill this goal, such as physical mockups, computerized visualization, and usability testing. In this analysis, a usability test is conducted to test how usable a website is to users who are and are not familiar with it. The testing is performed using participants and Morae software to record and analyze the results. This analysis will be a preliminary test of the usability lab in preparation for use in new spacecraft programs, NASA Enterprise, or other NASA websites. The usability lab project is divided into two parts: integration of the usability lab and a preliminary test of the usability lab.

Cheng, Yiwei↗