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NASA NTRS2014

Assessing the Risk of Crew Injury Due to Dynamic Loads During Spaceflight

Spaceflight requires tremendous amounts of energy to achieve Earth orbit and to attain escape velocity for interplanetary missions. Although the majority of the energy is managed in such a way as to limit the accelerations on the crew, several mission phases may result in crew exposure to dynamic loads. In the automotive industry, risk of serious injury can be tolerated because the probability of a crash is remote each time a person enters a vehicle, resulting in a low total risk of injury. For spaceflight, the level of acceptable injury risk must be lower to achieve a low total risk of injury because the dynamic loads are expected on each flight. To mitigate the risk of injury due to dynamic loads, the NASA Human Research Program has developed a research plan to inform the knowledge gaps and develop relevant tools for assessing injury risk. The risk of injury due to dynamic loads can be further subdivided into extrinsic and intrinsic risk factors. Extrinsic risk factors include the vehicle dynamic profile, seat and restraint design, and spacesuit design. Human tolerance to loads varies considerably depending on the direction, amplitude, and rise-time of acceleration therefore the orientation of the body to the dynamic vector is critical to determining crew risk of injury. Although a particular vehicle dynamic profile may be safe for a particular design, the seat, restraint, and suit designs can affect the risk of injury due to localized effects. In addition, characteristics intrinsic to the crewmember may also contribute to the risk of injury, such as crewmember sex, age, anthropometry, and deconditioning due to spaceflight, and each astronaut may have a different risk profile because of these factors. The purpose of the research plan is to address any knowledge gaps in the risk factors to mitigate injury risk. Methods for assessing injury risk have been well documented in other analogous industries and include human volunteer testing, human exposure to dynamic environments, post-mortem human subject (PMHS) testing, animal testing, anthropomorphic test devices (ATD), dynamic models of the human, numerical models of ATDs, and numerical models of the human. Each has inherent strengths and limitations. For example, human volunteer testing is advantageous because a population can be selected that is similar to the astronaut corps; however, because of the inherent ethical limitations, only sub-injurious conditions can be tested. PMHSs can be tested in a variety of conditions including injurious levels, but the responses are not completely analogous to living human subjects. In addition, it is exceedingly difficult to select a PMHS population that is similar to the astronaut corps. ATDs are currently widely used in the automotive industry and military because they are highly repeatable and durable. Unfortunately, because they are mechanical models of the human body, the biofidelity of the responses are limited to dynamic conditions used to validate the ATD. Numerical models of the ATD, in addition to the strengths and limitations for ATDs, are easy to use for a variety of designs before a design is fabricated, but also have additional limitations for ATDs, are easy to use for a variety of designs before a design is fabricated, but also have additional uncertainty. Dynamic models are simple and easy to use, but do not account for localized effects of the seat and suit. Finally, numerical models of the human have the potential to have the most advantages; however, the current models are not validated for the conditions expected during spaceflight. To properly assess spaceflight conditions with numerical human models, human data would be needed to optimize the model responses for those conditions. Using the appropriate assessment method with the knowledge gained for each risk factor, an appropriate approach for mitigating the risk of injury due to dynamic loads can be developed ensuring crew safety in future NASA vehicles.

Somers, J. T.↗
NASA NTRS2013

The Insertion of Human Factors Concerns into NextGen Programmatic Decisions

Since the costs of proposed improvements in air traffic management exceed available funding, FAA decision makers must select and prioritize what actually gets implemented. We discuss a set of methods to help forecast operational and human performance issues and benefits before new automation is introduced. This strategy could minimize the impact of politics, assist decision makers in selecting and prioritizing potential improvements, make the process more transparent and strengthen the link between the engineering and human factors domains.

air traffic control↗
NASA NTRS2006

A Comparative Study of Measuring Devices Used During Space Shuttle Processing for Inside Diameters

During Space Shuttle processing, discrepancies between vehicle dimensions and per print dimensions determine if a part should be refurbished, replaced or accepted "as-is." The engineer's job is to address each discrepancy by choosing the most accurate procedure and tool available, sometimes with up to ten thousands of an inch tolerance. Four methods of measurement are commonly used at the Kennedy Space Center: 1) caliper, 2) mold impressions, 3) optical comparator, 4) dial bore gage. During a problem report evaluation, uncertainty arose between methods after measuring diameters with variations of up to 0.0004" inches. The results showed that computer based measuring devices are extremely accurate, but when human factor is involved in determining points of reference, the results may vary widely compared to more traditional methods. iv

Rodriguez, Antonio↗
NASA NTRS1988

Cockpit automation

The aims and methods of aircraft cockpit automation are reviewed from a human-factors perspective. Consideration is given to the mixed pilot reception of increased automation, government concern with the safety and reliability of highly automated aircraft, the formal definition of automation, and the ground-proximity warning system and accidents involving controlled flight into terrain. The factors motivating automation include technology availability; safety; economy, reliability, and maintenance; workload reduction and two-pilot certification; more accurate maneuvering and navigation; display flexibility; economy of cockpit space; and military requirements.

Wiener, Earl L.↗
NASA NTRS2016

Tailoring a Human Reliability Analysis to Your Industry Needs

Companies at risk of accidents caused by human error that result in catastrophic consequences include: airline industry mishaps, medical malpractice, medication mistakes, aerospace failures, major oil spills, transportation mishaps, power production failures and manufacturing facility incidents. Human Reliability Assessment (HRA) is used to analyze the inherent risk of human behavior or actions introducing errors into the operation of a system or process. These assessments can be used to identify where errors are most likely to arise and the potential risks involved if they do occur. Using the basic concepts of HRA, an evolving group of methodologies are used to meet various industry needs. Determining which methodology or combination of techniques will provide a quality human reliability assessment is a key element to developing effective strategies for understanding and dealing with risks caused by human errors. There are a number of concerns and difficulties in "tailoring" a Human Reliability Assessment (HRA) for different industries. Although a variety of HRA methodologies are available to analyze human error events, determining the most appropriate tools to provide the most useful results can depend on industry specific cultures and requirements. Methodology selection may be based on a variety of factors that include: 1) how people act and react in different industries, 2) expectations based on industry standards, 3) factors that influence how the human errors could occur such as tasks, tools, environment, workplace, support, training and procedure, 4) type and availability of data, 5) how the industry views risk & reliability, and 6) types of emergencies, contingencies and routine tasks. Other considerations for methodology selection should be based on what information is needed from the assessment. If the principal concern is determination of the primary risk factors contributing to the potential human error, a more detailed analysis method may be employed versus a requirement to provide a numerical value as part of a probabilistic risk assessment. Industries involved with humans operating large equipment or transport systems (ex. railroads or airlines) would have more need to address the man machine interface than medical workers administering medications. Human error occurs in every industry; in most cases the consequences are relatively benign and occasionally beneficial. In cases where the results can have disastrous consequences, the use of Human Reliability techniques to identify and classify the risk of human errors allows a company more opportunities to mitigate or eliminate these types of risks and prevent costly tragedies.

DeMott, D. L.↗
NASA NTRS2015

Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

Machine learning↗
NASA NTRS1989

A human factors evaluation of Extravehicular Activity gloves

One of the major problems faced in Extravehicular Activity (EVA) glove development has been the absence of concise and reliable methods to measure the effects of EVA gloves on human-hand capabilities. NASA has sponsored a program to develop a standardized set of tests designed to assess EVA-gloved hand capabilities in six performance domains: Range of Motion, Strength, Tactile Perception, Dexterity, Fatigue, and Comfort. Based upon an assessment of general human-hand functioning and EVA task requirements, several tests within each performance domain were developed to provide a comprehensive evaluation. All tests were designed to be conducted in a glove box with the bare hand, an EVA glove without pressure, an EVA glove at operation pressure. Thus, the differential effect on performance of the glove with and without pressure was tested. Bare hand performance was used to 'calibrate' the effects. Ten subjects participated in the test setup as a repeated-measures experimental design. The paper will report the results of the test program.

O'Hara, John M.↗
NASA NTRS1993

Piloting Vertical Flight Aircraft: A Conference on Flying Qualities and Human Factors

This document contains papers from a specialists' meeting entitled "Piloting Vertical Flight Aircraft. A Conference on Flying Qualities and Human Factors." The conference was co-sponsored by the American Helicopter Society--San Francisco Bay Chapter, and the NASA Ames Research Center. It was held January 20-22, 1993 at the Sheraton Hotel Fisherman's Wharf, San Francisco, California. Vertical flight aircraft, including helicopters and a variety of Vertical Takeoff and Landing (VTOL) concepts, place unique requirements on human perception, control, and performance for the conduct of their design missions. The intent of this conference was to examine, for these vehicles, advances in: (1) design of flight control systems for ADS-33C standards; (2) assessment of human factors influences of cockpit displays and operational procedures; (3) development of VTOL design and operational criteria; and (4) development of theoretical methods or models for predicting pilot-vehicle performance and mission suitability. Recognizing that human capabilities and limitations form an integral aspect of the operations for these classes of vehicles, a secondary goal of the conference was to provide an initial venue for enhanced interaction between human factors and handling qualities specialists.

Helicopter↗
NASA NTRS2006

Human Factors Lessons Learned on the International Space Station

Experience on International Space Station (ISS) provides many important lessons for future space flight. NASA human factors engineers have been systematically collecting lessons learned from crew debriefs, as well as working with ground support teams to continuously improve crew operations. This paper describes the methods for collecting data from debriefs, lessons learned through that process, and an example of a technology development task funded through the Space Human Factors Engineering (SHFE) program element in response to an identified operational need. Each ISS increment crew spends many hours after the flight answering questions from the various subsystem leads. The Flight Crew Integration subsystem lead asks questions specific to human factors and habitability issues. In addition, crew comments on many other subsystems provide insight into interface designs, operability and maintainability. The debrief comments are unique to each crew, and must be categorized to provide operational lessons learned. Personal identifiers are removed and comments aggregated to separate consistent issues from personal preferences. Examples will be given, and the procedure for incorporating the lessons into requirements and guidelines for the next human space vehicle will be described. In flight, very few astronauts are medical doctors. Written medical procedures during flight need to be easy to follow and quick to understand. The problem was analyzed as part of a SHFE task. Organization was analyzed and reorganizations were created and tested. Results will be reported. The ISS is a very important analog for planning future long-term missions. Collection of data from debriefs, studying the lessons learned and focusing on requirements for future missions are examples of the accomplishments through the SHFE program.

Woolford, Barbara↗
NASA NTRS1996

A Framework for Modeling Human-Machine Interactions

Modern automated flight-control systems employ a variety of different behaviors, or modes, for managing the flight. While developments in cockpit automation have resulted in workload reduction and economical advantages, they have also given rise to an ill-defined class of human-machine problems, sometimes referred to as 'automation surprises'. Our interest in applying formal methods for describing human-computer interaction stems from our ongoing research on cockpit automation. In this area of aeronautical human factors, there is much concern about how flight crews interact with automated flight-control systems, so that the likelihood of making errors, in particular mode-errors, is minimized and the consequences of such errors are contained. The goal of the ongoing research on formal methods in this context is: (1) to develop a framework for describing human interaction with control systems; (2) to formally categorize such automation surprises; and (3) to develop tests for identification of these categories early in the specification phase of a new human-machine system.

Shafto, Michael G.↗
NASA NTRS2010

Constraint and Flight Rule Management for Space Mission Operations

The exploration of space is one of the most fascinating domains to study from a human factors perspective. Like other complex work domains such as aviation (Pritchett and Kim, 2008), air traffic management (Durso and Manning, 2008), health care (Morrow, North, and Wickens, 2006), homeland security (Cooke and Winner, 2008), and vehicle control (Lee, 2006), space exploration is a large-scale sociotechnical work domain characterized by complexity, dynamism, uncertainty, and risk in real-time operational contexts (Perrow, 1999; Woods et al, 1994). Nearly the entire gamut of human factors issues - for example, human-automation interaction (Sheridan and Parasuraman, 2006), telerobotics, display and control design (Smith, Bennett, and Stone, 2006), usability, anthropometry (Chaffin, 2008), biomechanics (Marras and Radwin, 2006), safety engineering, emergency operations, maintenance human factors, situation awareness (Tenney and Pew, 2006), crew resource management (Salas et al., 2006), methods for cognitive work analysis (Bisantz and Roth, 2008) and the like -- are applicable to astronauts, mission control, operational medicine, Space Shuttle manufacturing and assembly operations, and space suit designers as they are in other work domains (e.g., Bloomberg, 2003; Bos et al, 2006; Brooks and Ince, 1992; Casler and Cook, 1999; Jones, 1994; McCurdy et al, 2006; Neerincx et aI., 2006; Olofinboba and Dorneich, 2005; Patterson, Watts-Perotti and Woods, 1999; Patterson and Woods, 2001; Seagull et ai, 2007; Sierhuis, Clancey and Sims, 2002). The human exploration of space also has unique challenges of particular interest to human factors research and practice. This chapter provides an overview of those issues and reports on some of the latest research results as well as the latest challenges still facing the field.

Barreiro, J.↗
NASA NTRS2010

Human Factors in Space Exploration

The exploration of space is one of the most fascinating domains to study from a human factors perspective. Like other complex work domains such as aviation (Pritchett and Kim, 2008), air traffic management (Durso and Manning, 2008), health care (Morrow, North, and Wickens, 2006), homeland security (Cooke and Winner, 2008), and vehicle control (Lee, 2006), space exploration is a large-scale sociotechnical work domain characterized by complexity, dynamism, uncertainty, and risk in real-time operational contexts (Perrow, 1999; Woods et ai, 1994). Nearly the entire gamut of human factors issues - for example, human-automation interaction (Sheridan and Parasuraman, 2006), telerobotics, display and control design (Smith, Bennett, and Stone, 2006), usability, anthropometry (Chaffin, 2008), biomechanics (Marras and Radwin, 2006), safety engineering, emergency operations, maintenance human factors, situation awareness (Tenney and Pew, 2006), crew resource management (Salas et aI., 2006), methods for cognitive work analysis (Bisantz and Roth, 2008) and the like -- are applicable to astronauts, mission control, operational medicine, Space Shuttle manufacturing and assembly operations, and space suit designers as they are in other work domains (e.g., Bloomberg, 2003; Bos et al, 2006; Brooks and Ince, 1992; Casler and Cook, 1999; Jones, 1994; McCurdy et ai, 2006; Neerincx et aI., 2006; Olofinboba and Dorneich, 2005; Patterson, Watts-Perotti and Woods, 1999; Patterson and Woods, 2001; Seagull et ai, 2007; Sierhuis, Clancey and Sims, 2002). The human exploration of space also has unique challenges of particular interest to human factors research and practice. This chapter provides an overview of those issues and reports on sorne of the latest research results as well as the latest challenges still facing the field.

FROM↗
NASA NTRS1984

Preparing for the unexpected - A psychologist's case for improved training

In the procedures designed to minimize human errors that lead to aircraft incidents, the improved human factor engineering and automation approaches must be supplemented by new training methods. Changes are suggested in the preprogrammed training principles which are currently based almost exclusively on the procedures-oriented environment, with insufficient training for cognitive processing and awareness. Use of the Line-Oriented Flight Training procedure, in which a training simulator is supplemented by a highly structured script or scenario to simulate the total line operational environment for the purpose of simultaneously training the entire flight crew, offers one way of providing pilots and other crewmembers with the experience of dealing with unexpected or stressful events. Of primary importance is maximal coordination between the aircraft captain and other crewmembers during the flight, which puts emphasis on the importance of teamwork and personal relations among all other crewmembers. The current FARs governing training and proficiency will have to be modified to accommodate new training appoaches.

Foushee, H. C.↗
NASA NTRS1991

Empirical foundations and sensitivity testing - Is it enough for the 90's?

The FAA, in conjunction with the DOD, NASA, and the UK's Civil Aviation Administration, has devised a National Plan for Aviation Human Factors (NPAHF). Attention is presently given to the scope of research methods that are most useful for implementing NPAHF. Program managers in industry, government, and academia should identify tradeoffs intrinsic to the assembly of such methods, and justify the rationale for their choices in their research proposals.

Jenkins, James P.↗
NASA NTRS1997

Human Factors Research Under Ground-Based and Space Conditions

Session TP2 includes short reports concerning: (1) Human Factors Engineering of the International space Station Human Research Facility; (2) Structured Methods for Identifying and Correcting Potential Human Errors in Space operation; (3) An Improved Procedure for Selecting Astronauts for Extended Space Missions; (4) The NASA Performance Assessment Workstation: Cognitive Performance During Head-Down Bedrest; (5) Cognitive Performance Aboard the Life and Microgravity Spacelab; and (6) Psychophysiological Reactivity Under MIR-Simulation and Real Micro-G.

Source record↗
NASA NTRS2023

Research and Technology Challenges for Human Data Analysts in Future Safety Management Systems

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.

Chad L Stephens↗
NASA NTRS1987

Human factor design of habitable space facilities

Current fundamental and applied habitability research conducted as part of the U.S. space program is reviewed with emphasis on methods, findings, and applications of the results to the planning and design of the International Space Station. The discussion covers the following six concurrent directions of habitability research: operational simulation, functional interior decor research, space crew privacy requirements, interior layout and configuration analysis, human spatial habitability model, and analogous environments research.

Clearwater, Yvonne A.↗
NASA NTRS2000

Human versus automation in responding to failures: an expected-value analysis

A simple analytical criterion is provided for deciding whether a human or automation is best for a failure detection task. The method is based on expected-value decision theory in much the same way as is signal detection. It requires specification of the probabilities of misses (false negatives) and false alarms (false positives) for both human and automation being considered, as well as factors independent of the choice--namely, costs and benefits of incorrect and correct decisions as well as the prior probability of failure. The method can also serve as a basis for comparing different modes of automation. Some limiting cases of application are discussed, as are some decision criteria other than expected value. Actual or potential applications include the design and evaluation of any system in which either humans or automation are being considered.

NASA Discipline Space Human Factors↗