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Optimal role of man in space, allocation of men and machines in aerospace systems, and descriptive model for determining optimal human performance
The ultimate success of future human space exploration missions is dependent on the ability to perform extravehicular activity (EVA) tasks effectively, efficiently, and safely, whether those tasks represent a nominal mode of operation or a contingency capability. To optimize EVA systems for the best human performance, it is critical to study the effects of varying key factors such as suit center of gravity (CG), suit mass, and gravity level. During the 2-week NASA Extreme Environment Mission Operations (NEEMO) 14 mission, four crewmembers performed a series of EVA tasks under different simulated EVA suit configurations and used full-scale mockups of a Space Exploration Vehicle (SEV) rover and lander. NEEMO is an underwater spaceflight analog that allows a true mission-like operational environment and uses buoyancy effects and added weight to simulate different gravity levels. Quantitative and qualitative data collected during NEEMO 14, as well as from spacesuit tests in parabolic flight and with overhead suspension, are being used to directly inform ongoing hardware and operations concept development of the SEV, exploration EVA systems, and future EVA suits. OBJECTIVE: To compare human performance across different weight and CG configurations. METHODS: Four subjects were weighed out to simulate reduced gravity and wore either a specially designed rig to allow adjustment of CG or a PLSS mockup. Subjects completed tasks including level ambulation, incline/decline ambulation, standing from the kneeling and prone position, picking up objects, shoveling, ladder climbing, incapacitated crewmember handling, and small and large payload transfer. Subjective compensation, exertion, task acceptability, and duration data as well as photo and video were collected. RESULTS: There appear to be interactions between CG, weight, and task. CGs nearest the subject s natural CG are the most predictable in terms of acceptable performance across tasks. Future research should focus on understanding the interactions between CG, mass, and subject differences.
NASA's Orion Crew Exploration Vehicle (CEV) will provide transportation for crew and cargo to and from destinations in support of the Constellation Architecture Design Reference Missions. Discrete Event Simulation (DES) is one of the design methods NASA employs for crew performance of the CEV. During the early development of the CEV, NASA and its prime Orion contractor Lockheed Martin (LM) strived to seek an effective low-cost method for developing and validating human performance DES models. This paper focuses on the method developed while creating a DES model for the CEV Rendezvous, Proximity Operations, and Docking (RPOD) task to the International Space Station. Our approach to validation was to attack the problem from several fronts. First, we began the development of the model early in the CEV design stage. Second, we adhered strictly to M&S development standards. Third, we involved the stakeholders, NASA astronauts, subject matter experts, and NASA's modeling and simulation development community throughout. Fourth, we applied standard and easy-to-conduct methods to ensure the model's accuracy. Lastly, we reviewed the data from an earlier human-in-the-loop RPOD simulation that had different objectives, which provided us an additional means to estimate the model's confidence level. The results revealed that a majority of the DES model was a reasonable representation of the current CEV design.
The purpose of the effort outlined in this briefing was to determine whether models exist or can be developed that can be used to address aviation automation issues. A multidisciplinary team has been assembled to undertake this effort, including experts in human performance, team/crew, and aviation system modeling, and aviation data used as input to such models. The project consists of two phases, a requirements assessment phase that is designed to determine the feasibility and utility of alternative modeling efforts, and a model development and evaluation phase that will seek to implement the plan (if a feasible cost effective development effort is found) that results from the first phase. Viewgraphs are given.
Manned space operations require that the many automated subsystems of a space platform be controllable by a limited number of personnel. To minimize the interaction required of these operators, artificial intelligence techniques may be applied to embed a human performance model within the automated, or semi-automated, systems, thereby allowing the derivation of operator intent. A similar application has previously been proposed in the domain of fighter piloting, where the demand for pilot intent derivation is primarily a function of limited time and high workload rather than limited operators. The derivation and propagation of pilot intent is presented as it might be applied to some programs.
Pilots are able to extract information about their vehicle motion and environmental structure from dynamic transformations in the out-the-window scene. In this presentation, we focus on the information in the optic flow which specifies vehicle heading and distance to objects in the environment, scaled to a temporal metric. In particular, we are concerned with modeling how the human operators extract the necessary information, and what factors impact their ability to utilize the critical information. In general, the psychophysical data suggest that the human visual system is fairly robust to degradations in the visual display, e.g., reduced contrast and resolution or restricted field of view. However, extraneous motion flow, i.e., introduced by sensor rotation, greatly compromises human performance. The implications of these models and data for enhanced/synthetic vision systems are discussed.
We present a study of weather-related incident reports submitted to NASA’s Aviation Safety Reporting System (ASRS) by air carrier pilots and air traffic controllers in the US. Using specific examples, we examine the weather-related risks reported and the relevant aspects of human performance and resilience management exhibited during these incidents. We describe our methods of lesson extraction to maximize learning, including the use of advance algorithms, and we conclude with a review of the weather-related risks encountered and the resilient practices employed to mitigate them.
In this session, Session JP1, the discussion focuses on the following topics: New Developments in the Assessment of the Risk of Decompression Sickness in Null Gravity During Extravehicular Activity; The Dynamic of Physiological Reactions of Cosmonauts Under the Influence of Repeated EVA Workouts, The Russian Experience; Medical Emergencies in Space; The Evolution from 'Physiological Adequacy' to 'Physiological Tuning'; Five Zones of Symmetrical and Asymmetrical Conflicting Temperatures on the Human Body, Physiological Consequences; Human Performance and Subjective Perception in Nonuniform Thermal Conditions; The Hand as a Control System, Implications for Hand-Finger Dexterity During Extended EVA; and Understanding the Skill of Extravehicular Mass Handling.
Stress, fatigue, and workload affect worker performance. NSF reported that 61% of respondents state losing concentration at work while 79% occasionally or frequently made errors as a result of being fatigued. Shift work, altered work schedules, long hours of continuous wakefulness, and sleep loss can create sleep and circadian disruptions that degrade waking fundions causing stress and fatigue. Review of the literature has proven void of information that links the combined effects of fatigue, stress, and workload to human performance. This paper will address which occupational factors within stress, fatigue, and workload were identified as occupational contributors to performance changes. The results of this research will be apglied to underlying models and algorithms that will help predict performance changes in control room operators.
The results of several experiments are presented in abstract form. These studies are critical for the interpretation and acceptance of flight based science to be conducted by the Behavior and Performance project. Some representative titles are as follow: External audio for IBM/PC compatible computers; A comparative assessment of psychomotor performance (target prediction by humans and macaques); Response path (a dependent measure for computer maze solving and other tasks); Behavioral asymmetries of psychomotor performance in Rhesus monkey (a dissociation between hand preference and skill); Testing primates with joystick based automated apparatus; and Environmental enrichment and performance assessment for ground or flight based research with primates;
The scope of this report is limited to the following: (1) assessing the feasibility of the assumptions for crew productivity during the intra-vehicular activities and extra-vehicular activities; (2) estimating the appropriate level of automation and robotics to accomplish balanced man-machine, cost-effective operations in space; (3) identifying areas where conceptually different approaches to the use of people and machines can leverage the benefits of the scenarios; and (4) recommending modifications to scenarios or developing new scenarios that will improve the expected benefits. The FY89 special assessments are grouped into the five categories shown in the report. The high level system analyses for Automation & Robotics (A&R) and Human Performance (HP) were performed under the Case Studies Technology Assessment category, whereas the detailed analyses for the critical systems and high leverage development areas were performed under the appropriate operations categories (In-Space Vehicle Operations or Planetary Surface Operations). The analysis activities planned for the Science Operations technology areas were deferred to FY90 studies. The remaining activities such as analytic tool development, graphics/video demonstrations and intelligent communicating systems software architecture were performed under the Simulation & Validations category.
Background: Ensuring the validity of results from funded programs is a critical concern for agencies that sponsor biological research. In recent years, the open science movement has sought to promote reproducibility by encouraging sharing not only of finished manuscripts but also of data and code supporting their findings. While these innovations have lent support to third-party efforts to replicate calculations underlying key results in the scientific literature, fields of inquiry where privacy considerations or other sensitivities preclude the broad distribution of raw data or analysis may require a more targeted approach to promote the quality of research output. Methods: We describe efforts oriented toward this goal that were implemented in one human performance research program, Measuring Biological Aptitude, organized by the Defense Advanced Research Project Agency's Biological Technologies Office. Our team implemented a four-pronged independent verification and validation (IV&V) strategy including 1) a centralized data storage and exchange platform, 2) quality assurance and quality control (QA/QC) of data collection, 3) test and evaluation of performer models, and 4) an archival software and data repository. Results: Our IV&V plan was carried out with assistance from both the funding agency and participating teams of researchers. QA/QC of data acquisition aided in process improvement and the flagging of experimental errors. Holdout validation set tests provided an independent gauge of model performance. Conclusions: In circumstances that do not support a fully open approach to scientific criticism, standing up independent teams to cross-check and validate the results generated by primary investigators can be an important tool to promote reproducibility of results.
The Center of Excellence (COE) was created in 1984 to facilitate active collaboration between the scientists at Ames Research Center and the Stanford Psychology Department. As this document will review, over that period of time, the COE served its function well. Funds from the Center supported a large number of projects over the last ten years. Many of the people who were supported by the Center Have gone on to distinguished research careers in government, industry and university. In fact, several of the people currently working at NASA Ames were initially funded by the Center mechanism, which served as a useful vehicle for attracting top quality candidates and supporting their research efforts. We are grateful for NASA's support over the years. As we reviewed in the reports for each year, the COE budget generally provided a portion of the true costs of the individual research project. Hence, the funds from the COE were leveraged with funds from industry and other government agencies. In this way, we feel that all parties benefitted greatly from the collaborative spirit and interactive aspects of the COE. The portion of the support from NASA was particularly important in helping members of the COE to set aside the time to publish papers and communicate advances in our understanding of human performance in NASA-related missions.
The Center of Excellence (COE) was created in 1984 to facilitate active collaboration between the scientists at Ames Research Center and the Stanford Psychology Department. As this document will review, over that period of time, the COE served its function well. Funds from the Center supported a large number of projects over the last ten years. Many of the people who were supported by the Center have gone on to distinguished research careers in government, industry and university. In fact, several of the people currently working at NASA Ames were initially funded by the Center mechanism, which served as a useful vehicle for attracting top quality candidates and supporting their research efforts. We are grateful for NASA's support over the years. As we reviewed in the reports for each year, the COE budget generally provided a portion of the true costs of the individual research projects. Hence, the funds from the COE were leveraged with funds from industry and other government agencies. In this way, we feel that all parties benefitted greatly from the collaborative spirit and interactive aspects of the COE. The portion of the support from NASA was particularly important in helping members of the COE to set aside the time to publish papers and communicate advances in our understanding of human performance in NASA-related missions.
In 2020 the world was impacted by COVID-19. How work is done was dramatically changed for a large portion of the workforce. Many have made the abrupt shift to working from their home while others have adapted to COVID protocols (such as social distancing and wearing facemasks). Fundamentally, Human and Organizational Performance Improvement concepts and science did not change – only the delivery method changed (in some cases). The primary effect has been to accelerate dynamics already present in society – from e-commerce to online education to remote healthcare. Change is never easy for humans. However, remote work should be viewed as an opportunity; not a fix to a problem. In this Best Practice paper, you’ll get practical suggestions to help you reduce errors and improve Human Performance in remote, socially-distanced “work-from-home” environments.
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A predictive model of human operator performance (flight crew and air traffic control (ATC)) has been developed and applied in order to evaluate the impact of automation developments in flight management and air traffic control. The model is used to predict the performance of a two person flight crew and the ATC operators generating and responding to clearances aided by the Center TRACON Automation System (CTAS). The purpose of the modeling is to support evaluation and design of automated aids for flight management and airspace management and to predict required changes in procedure both air and ground in response to advancing automation in both domains. Additional information is contained in the original extended abstract.