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MIDAS, Man-machine Integration Design and Analysis System, is a unique combination of software tools aimed at reducing design cycle time, supporting quantitative predictions of human-system effectiveness and improving the design of crew stations and their associated operating procedures. This project is supported jointly by the US Army and NASA.
With NASA's new Vision for Exploration to send humans beyond Earth orbit, it is critical to consider the human as a system that demands early and continuous user involvement, and an iterative prototype/test/redesign process. Addressing human-system interface issues early on can be very cost effective even cost reducing when performed early in the design and development cycle. To achieve this goal within Crew Exploration Vehicle (CEV) Project Office, human engineering (HE) team is formed. Key tasks are to apply HE requirements and guidelines to hardware/software, and provide HE design, analysis and evaluation of crew interfaces. Initial activities included many practice-orientated evaluations using low-fidelity CEV mock-ups. What follows is a description of such evaluations that focused on a HE requirement regarding Net Habitable Volume (NHV). NHV is defined as the total remaining pressurized volume available to on-orbit crew after accounting for the loss of volume due to deployed hardware and structural inefficiencies which decrease functional volume. The goal of the NHV evaluations was to develop requirements providing sufficient CEV NHV for crewmembers to live and perform tasks in support of mission goals. Efforts included development of a standard NHV calculation method using computer models and physical mockups, and crew/ stakeholder evaluations. Nine stakeholders and ten crewmembers participated in the unsuited evaluations. Six crewmembers also participated in a suited evaluation. The mock-up was outfitted with volumetric representation of sub-systems such as seats, and stowage bags. Thirteen scenarios were developed to represent mission/crew tasks and considered to be primary volume drivers (e.g., suit donning) for the CEV. Unsuited evaluations included a structured walkthrough of these tasks. Suited evaluations included timed donning of the existing launch and entry suit to simulate a contingency scenario followed by doffing/ stowing of the suits. All mockup evaluations were videotaped. Structured questionnaires were used to document user interface issues and volume impacts of layout configuration. Computer model and physical measures of the NHV agreed within 1 percent. This included measurement of the gross habitable volume, subtraction of intrusive volumes, and other non-habitable spaces. Calculation method developed was validated as a standard means of measuring NHV, and was recommended as a verification method for the NHV requirements. Evaluations confirmed that there was adequate volume for unsuited scenarios and suit donning/ doffing activity. Seats, suit design stowage and waste hygiene system noted to be critical volume drivers. The low-fidelity mock-up evaluations along with human modeling analysis generated discussions that will lead to high-level systems requirements and human-centered design decisions. This approach allowed HE requirements and operational concepts to evolve in parallel with engineering system concepts and design requirements. As the CEV design matures, these evaluations will continue and help with design decisions, and assessment, verification and validation of HE requirements.
The Habitability & Environmental Factors and Space Medicine Divisions have developed the Space Flight Human System Standard (SFHSS) (NASA-STD-3001) to replace NASA-STD-3000 as a new NASA standard for all human spaceflight programs. The SFHSS is composed of 2 volumes. Volume 1, Crew Health, contains medical levels of care, permissible exposure limits, and fitness for duty criteria, and permissible outcome limits as a means of defining successful operating criteria for the human system. Volume 2, Habitability and Environmental Health, contains environmental, habitability and human factors standards. Development of the Human Integration Design Handbook (HIDH), a companion to the standard, is currently under construction and entails the update and revision of NASA-STD-3000 data. This new handbook will, in the fashion of NASA STD-3000, assist engineers and designers in appropriately applying habitability, environmental and human factors principles to spacecraft design. Organized in a chapter-module-element structure, the HIDH will provide the guidance for the development of requirements, design considerations, lessons learned, example solutions, background research, and assist in the identification of gaps and research needs in the disciplines. Subject matter experts have been and continue to be solicited to participate in the update of the chapters. The purpose is to build the HIDH with the best and latest data, and provide a broad representation from experts in industry, academia, the military and the space program. The handbook and the two standards volumes work together in a unique way to achieve the required level of human-system interface. All new NASA programs will be required to meet Volumes 1 and 2. Volume 2 presents human interface goals in broad, non-verifiable standards. Volume 2 also requires that each new development program prepare a set of program-specific human factors requirements. These program-specific human and environmental factors requirements must be verifiable and tailored to assure the new system meets the Volume 2 standards. Programs will use the HIDH to write their verifiable program-specific requirements.
When designing any human-system interface, it is critical to provide realistic anthropometry to properly represent how a person fits within a given space. This study aimed to identify a minimum number of boundary manikins or representative models of subjects anthropometry from a target population, which would realistically represent the population. The boundary manikin anthropometry was derived using, Principal Component Analysis (PCA). PCA is a statistical approach to reduce a multi-dimensional dataset using eigenvectors and eigenvalues. The measurements used in the PCA were identified as those measurements critical for suit and cockpit design. The PCA yielded a total of 26 manikins per gender, as well as their anthropometry from the target population. Reduction techniques were implemented to reduce this number further with a final result of 20 female and 22 male subjects. The anthropometry of the boundary manikins was then be used to create 3D digital models (to be discussed in subsequent papers) intended for use by designers to test components of their space suit design, to verify that the requirements specified in the Human Systems Integration Requirements (HSIR) document are met. The end-goal is to allow for designers to generate suits which accommodate the diverse anthropometry of the user population.
NASA created a new approach for human system integration and human performance standards. NASA created two documents a standard and a reference handbook. The standard is titled NASA Space Flight Human-System Standard (SFHSS) and consists of two-volumes: Volume 1- Crew Health This volume covers standards needed to support astronaut health (medical care, nutrition, sleep, exercise, etc.) Volume 2 Human Factors, Habitability and Environmental Health This volume covers the standards for system design that will maintain astronaut performance (ie., environmental factors, design of facilities, layout of workstations, and lighting requirements). It includes classic human factors requirements. The new standards document is written in terms so that it is applicable to a broad range of present and future NASA systems. The document states that all new programs prepare system-specific requirements that will meet the general standards. For example, the new standard does not specify a design should accommodate specific percentiles of a defined population. Rather, NASA-STD-3001, Volume 2 states that all programs shall prepare program-specific requirements that define the user population and their size ranges. The design shall then accommodate the full size range of those users. The companion reference handbook, Human Integration Design Handbook (HIDH), was developed to capture the design consideration information from NASA-STD-3000, and adds spaceflight lessons learned, gaps in knowledge, example solutions, and suggests research to further mature specific disciplines. The HIDH serves two major purposes: HIDH is the reference document for writing human factors requirements for specific systems. HIDH contains design guidance information that helps insure that designers create systems which safely and effectively accommodate the capabilities and limitations of space flight crews.
Modeling and simulation (M&S) plays an important role when complex human-system notions are being proposed, developed and tested within the system design process. National Aeronautics and Space Administration (NASA) as an agency uses many different types of M&S approaches for predicting human-system interactions, especially when it is early in the development phase of a conceptual design. NASA Ames Research Center possesses a number of M&S capabilities ranging from airflow, flight path models, aircraft models, scheduling models, human performance models (HPMs), and bioinformatics models among a host of other kinds of M&S capabilities that are used for predicting whether the proposed designs will benefit the specific mission criteria. The Man-Machine Integration Design and Analysis System (MIDAS) is a NASA ARC HPM software tool that integrates many models of human behavior with environment models, equipment models, and procedural / task models. The challenge to model comprehensibility is heightened as the number of models that are integrated and the requisite fidelity of the procedural sets are increased. Model transparency is needed for some of the more complex HPMs to maintain comprehensibility of the integrated model performance. This will be exemplified in a recent MIDAS v5 application model and plans for future model refinements will be presented.
This slide presentation reviews the history, and development of NASA-STD-3001, NASA Space Flight Human-System Standard Human Factors, Habitability, and Environmental Health, and the related Human Integration Design Handbook. Currently being developed from NASA-STD-3000, this project standard currently in review will be available in two volumes, (i.e., Volume 1 -- VCrew Health and Volume 2 -- Human Factors, Habitability, and Environmental Health) and the handbook will be both available as a pdf file and as a interactive website.
This report identifies key human-performance-related issues associated with Next Generation Air Transportation System (NextGen) research in the NASA NextGen-Airspace Project. Four Research Focus Areas (RFAs) in the NextGen-Airspace Project - namely Separation Assurance (SA), Airspace Super Density Operations (ASDO), Traffic Flow Management (TFM), and Dynamic Airspace Configuration (DAC) - were examined closely. In the course of the research, it was determined that the identified human performance issues needed to be analyzed in the context of NextGen operations rather than through basic human factors research. The main gaps in human factors research in NextGen were found in the need for accurate identification of key human-systems related issues within the context of specific NextGen concepts and better design of the operational requirements for those concepts. By focusing on human-system related issues for individual concepts, key human performance issues for the four RFAs were identified and described in this report. In addition, mixed equipage airspace with components of two RFAs were characterized to illustrate potential human performance issues that arise from the integration of multiple concepts.
Human space exploration has never been more exciting than it is today. Human presence to outer worlds is becoming a reality as humans are leveraging much of our prior knowledge to the new mission of going to Mars. Exploring the solar system at greater distances from Earth than ever before will possess some unique challenges, which can be overcome thanks to the advances in modeling and simulation technologies. The National Aeronautics and Space Administration (NASA) is at the forefront of exploring our solar system. NASA's Human Research Program (HRP) focuses on discovering the best methods and technologies that support safe and productive human space travel in the extreme and harsh space environment. HRP uses various methods and approaches to answer questions about the impact of long duration missions on the human in space including: gravity's impact on the human body, isolation and confinement on the human, hostile environments impact on the human, space radiation, and how the distance is likely to impact the human. Predictive models are included in the HRP research portfolio as these models provide valuable insights into human-system operations. This paper will provide an overview of NASA's HRP and will present a number of projects that have used modeling and simulation to provide insights into human-system issues (e.g. automation, habitat design, schedules) in anticipation of space exploration.
In the development of intelligent systems that interact with humans, there is often confusion between how the system functions with respect to the humans it interacts with and how it interfaces with those humans. The former is a much deeper challenge than the latter it requires a system-level understanding of evolving human roles as well as an understanding of what humans need to know (and when) in order to perform their tasks. This talk will focus on some of the challenges in getting this right as well as on the type of research and development that results in successful human-autonomy teaming. Brief Bio: Dr. Alonso Vera is Chief of the Human Systems Integration Division at NASA Ames Research Center. His expertise is in human-computer interaction, information systems, artificial intelligence, and computational human performance modeling. He has led the design, development and deployment of mission software systems across NASA robotic and human space flight missions, including Mars Exploration Rovers, Phoenix Mars Lander, ISS, Constellation, and Exploration Systems. Dr. Vera received a Bachelor of Science with First Class Honors from McGill University in 1985 and a Ph.D. from Cornell University in 1991. He went on to a Post-Doctoral Fellowship in the School of Computer Science at Carnegie Mellon University from 1990-93.
Within the Human Research Program, one risk delineates the uncertainty surrounding crew working with automation and robotics in spaceflight. The Risk of Inadequate Design of Human and Automation/Robotic Integration (HARI) is concerned with the detrimental effects on crew performance due to ineffective user interfaces, system designs and/or functional task allocation, potentially compromising mission success and safety. Risk arises because we have limited experience with complex automation and robotics. One key gap within HARI, is the gap related to functional allocation. The gap states: We need to evaluate, develop, and validate methods and guidelines for identifying human-automation/robot task information needs, function allocation, and team composition for future long duration, long distance space missions. Allocations determine the human-system performance as it identifies the functions and performance levels required by the automation/robotic system, and in turn, what work the crew is expected to perform and the necessary human performance requirements. Allocations must take into account each of the human, automation, and robotic systems capabilities and limitations. Some functions may be intuitively assigned to the human versus the robot, but to optimize efficiency and effectiveness, purposeful role assignments will be required. The role of automation and robotics will significantly change in future exploration missions, particularly as crew becomes more autonomous from ground controllers. Thus, we must understand the suitability of existing function allocation methods within NASA as well as the existing allocations established by the few robotic systems that are operational in spaceflight. In order to evaluate future methods of robotic allocations, we must first benchmark the allocations and allocation methods that have been used. We will present 1) documentation of human-automation-robotic allocations in existing, operational spaceflight systems; and 2) To gather existing lessons learned and best practices in these role assignments, from spaceflight operational experience of crew and ground teams that may be used to guide development for future systems. NASA and other space agencies have operational spaceflight experience with two key Human-Automation-Robotic (HAR) systems: heavy lift robotic arms and planetary robotic explorers. Additionally, NASA has invested in high-fidelity rover systems that can carry crew, building beyond Apollo's lunar rover. The heavy lift robotic arms reviewed are: Space Station Remote Manipulator System (SSRMS), Japanese Remote Manipulator System (JEMRMS), and the European Robotic Arm (ERA, designed but not deployed in space). The robotic rover systems reviewed are: Mars Exploration Rovers, Mars Science Laboratory rover, and the high-fidelity K10 rovers. Much of the design and operational feedback for these systems have been communicated to flight controllers and robotic design teams. As part of the mitigating the HARI risk for future human spaceflight operations, we must document function allocations between robots and humans that have worked well in practice.
Human space exploration has never been more exciting than it is today. Human presence to outer worlds is becoming a reality as humans are leveraging much of our prior knowledge to the new mission of going to Mars. Exploring the solar system at greater distances from Earth than ever before will possess some unique challenges, which can be overcome thanks to the advances in modeling and simulation technologies. The National Aeronautics and Space Administration (NASA) is at the forefront of exploring our solar system. NASA's Human Research Program (HRP) focuses on discovering the best methods and technologies that support safe and productive human space travel in the extreme and harsh space environment. HRP uses various methods and approaches to answer questions about the impact of long duration missions on the human in space including: gravitys impact on the human body, isolation and confinement on the human, hostile environments impact on the human, space radiation, and how the distance is likely to impact the human. Predictive models are included in the HRP research portfolio as these models provide valuable insights into human-system operations. This paper will provide an overview of NASA's HRP and will present a number of projects that have used modeling and simulation to provide insights into human-system issues (e.g. automation, habitat design, schedules) in anticipation of space exploration.
Appropriate integration between automation and robotics systems and their human operators is essential for future space exploration. The Human Factors and Behavioral Performance Element of NASA’s Human Research Program requires a systematic understanding of the critical human-automation/robotic (HAR) integration, or HARI, design challenges for future space exploration. This document reports the results of a systematic assessment of the spaceflight-relevant HARI technologies and research topics addressing critical gaps in spaceflight-relevant HARI knowledge, and prioritizes research required for successful human performance and HAR integration. We reviewed relevant literature across the past ten years and interviewed ten subject matter experts to investigate the current state of HARI technology, challenges facing development, the state of HARI research across a wide range of fields, and opportunities for advancing the state of the art through directed research. This information was used to identify relevant HARI technologies and research topics, as well as factors to assess relative priority of HARI technologies. We worked with NASA stakeholders to weight the factors relevant to assessing HARI specific technologies. A multi-dimensional trade analysis was performed to objectively score HARI research topics and specific technologies to recommended investment priorities for NASA.
A goal of the Human Research Program (HRP) Exploration Medical Capability (ExMC) Element Systems Engineering (SE) team is to define the technical system needed to support crew medical system capabilities for future exploration missions, including a mission with lunar orbital and long duration surface operations. The medical system concept of operations (ConOps) is the starting point of creating a foundation for a medical system that meets Level of Care IV requirements, as defined by NASA’s space flight human-system standards. This ConOps illustrates how NASA can provide a Level of Care IV medical system for crews within the various cis-lunar orbit and lunar surface habitat environments. The use case scenarios included in this ConOps illustrate required medical system capabilities for Level of Care IV and enable the ExMC SE team to develop integrated medical system requirements and identify capabilities required to meet those requirements. This discussion will focus on how this concept of operations was constructed – by utilizing the lessons learned from ExMC’s existing Short-Duration Lunar Orbit Medical System Concept of Operations, by employing Model-Based Systems Engineering, and by collaborating with stakeholders in order to ensure the necessary assumptions were made.
In recent times, there has been a growing interest in resilience-based design. Resilience-based design operates on the concept that failures and unexpected events will happen, and when they occur, complex engineered systems should be able to operate within acceptable bounds and recover reasonably. Humans can contribute to the resilience of a system by quickly detecting unforeseen events and taking corrective measures. To this effect, researchers have proposed guidelines and design approaches that can help promote human-system resilience. However, there is no early design stage tool to validate if a system is indeed resilient after applying these guidelines and design methods. In this research, we integrate the Human Error and Functional Failure Reasoning (HEFFR) framework into the fmdtools toolkit to enable designers to model the combined (machine, human, and joint) failures, including their propagation and dynamic effects, during early design stages. This integrated tool also allows designers to model the effects of performance shaping factors, team dynamics, and human-machine interactions in systems of systems. A demonstrative example of a remotely operated rover is explored to demonstrate how this approach can be applied to understand resilience in complex engineered systems with human interactions.
Enabling new and novel concepts of operations for Advanced Air Mobility poses an important need to evolve current safety management systems (SMS) and is posited to be realized through advances in Machine Learning (ML) Data Sciences and Artificial Intelligence. The “In-time Aviation Safety Management System” (IASMS) concept of operations supports the need to evolve today’s SMS to become more tailorable, scalable, and interoperable in response to forecasted changes expected for the future airspace system. Key to IASMS is integration of proactive and predictive ML algorithms trained to provide “in time” detection and mitigation of hazards and emergent risks through new methods and novel data types. IASMS research and technology development includes human factors design considerations for these systems to include human-system teaming, innovations in human interfaces and management of complex digital data information, human-system interaction/model-based system engineering, and verification and validation for data assurance and trust.
Human thermal analysis and model development inform NASA’s space suit development, vehicle/habitat design, and survivability studies. Historically, human thermal models such as the 41-node metabolic man (METMAN) and the Wissler model have been standalone software tools developed with FORTRAN, a programming language known for its high performance in computationally intensive applications. Though efficient, these standalone programs pose challenges to coupled human-system analysis with detailed life support and thermal control subsystem models in other commercial software. This paper describes the conversion of the METMAN human thermal model from a standalone FORTRAN program to a model in Thermal Desktop (Ansys), a commonly used CAD-based simulation software for engineering that specializes in heat transfer, thermal radiation, and fluid flow analysis. This format was chosen to best facilitate model sharing and compatibility, enabling the direct integration of METMAN human thermal analysis with subsystem models across NASA programs and commercial partners.
The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.