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At least 217 records · Page 12

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NASA NTRS2010

Modeling the Effects of Stress: An Approach to Training

Stress is an integral element of the operational conditions experienced by combat medics. The effects of stress can compromise the performance of combat medics who must reach and treat their comrades under often threatening circumstances. Examples of these effects include tunnel vision, loss of motor control, and diminished hearing, which can result in an inability to perceive further danger, satisfactorily treat the casualty, and communicate with others. While many training programs strive to recreate this stress to aid in the experiential learning process, stress inducement may not always be feasible or desired. In addition, live simulations are not always a practical, convenient, and repeatable method of training. Instead, presenting situational training on a personal computer is proposed as an effective training platform in which the effects of stress can be addressed in a different way. We explore the cognitive and motor effects of stress, as well as the benefits of training for mitigating these effects in real life. While many training applications focus on inducing stress in order to "condition" the stress response, the author explores the possibilities of modeling stress to produce a similar effect. Can presenting modeled effects of stress help prepare or inoculate soldiers for stressful situations in which they must perform at a high level? This paper investigates feasibility of modeling stress and describes the preliminary design considerations of a combat medic training system that utilizes this method of battlefield preparation.

Cuper, Taryn↗

NASA NTRS2017

Autonomous Diagnostic Imaging Performed by Untrained Operators using Augmented Reality as a Form of "Just-in-Time" Training

We will address the Human Factors and Performance Team, "Risk of performance errors due to training deficiencies" by improving the JIT training materials for ultrasound and OCT imaging by providing advanced guidance in a detailed, timely, and user-friendly manner. Specifically, we will (1) develop an audio-visual tutorial using AR that guides non-experts through an abdominal trauma ultrasound protocol; (2) develop an audio-visual tutorial using AR to guide an untrained operator through the acquisition of OCT images; (3) evaluate the quality of abdominal ultrasound and OCT images acquired by untrained operators using AR guidance compared to images acquired using traditional JIT techniques (laptop-based training conducted before image acquisition); and (4) compare the time required to complete imaging studies using AR tutorials with images acquired using current JIT practices to identify areas for time efficiency improvements. Two groups of subjects will be recruited to participate in this study. Operator-subjects, without previous experience in ultrasound or OCT, will be asked to perform both procedures using either the JIT training with AR technology or the traditional JIT training via laptop. Images acquired by inexperienced operator-subjects will be scored by experts in that imaging modality for diagnostic and research quality; experts will be blinded to the form of JIT used to acquire the images. Operator-subjects also will be asked to submit feedback to improve the training modules used during the scans to improve future training modules. Scanned-subjects will be a small group individuals from whom all images will be acquired.

Martin, D. S.↗

NASA NTRS2017

Autonomous Diagnostic Imaging Performed by Untrained Operator Using Augmented Reality as a Form of "Just-in-Time" Training

Martin, David S.↗

NASA NTRS2016

Teamwork Training Needs Analysis for Long-Duration Exploration Missions

The success of future long-duration exploration missions (LDEMs) will be determined largely by the extent to which mission-critical personnel possess and effectively exercise essential teamwork competencies throughout the entire mission lifecycle (e.g., Galarza & Holland, 1999; Hysong, Galarza, & Holland, 2007; Noe, Dachner, Saxton, & Keeton, 2011). To ensure that such personnel develop and exercise these necessary teamwork competencies prior to and over the full course of future LDEMs, it is essential that a teamwork training curriculum be developed and put into place at NASA that is both 1) comprehensive, in that it targets all teamwork competencies critical for mission success and 2) structured around empirically-based best practices for enhancing teamwork training effectiveness. In response to this demand, the current teamwork-oriented training needs analysis (TNA) was initiated to 1) identify the teamwork training needs (i.e., essential teamwork-related competencies) of future LDEM crews, 2) identify critical gaps within NASA’s current and future teamwork training curriculum (i.e., gaps in the competencies targeted and in the training practices utilized) that threaten to impact the success of future LDEMs, and to 3) identify a broad set of practical nonprescriptive recommendations for enhancing the effectiveness of NASA’s teamwork training curriculum in order to increase the probability of future LDEM success.

Smith-Jentsch, Kimberly A.↗

NASA NTRS2018

Training for Mars

Spaceflight training for Apollo era and Space Shuttle missions was designed to train crew on specific tasks for their short duration missions. The training mottos included "practice, practice, practice" and "hammering it in." However, training for longer duration ISS (International Space Station) missions has been more challenging. There is clear documentation that tasks and skills trained pre-flight are not all retained throughout the mission, and this lack of training retention is augmented with real-time ground support. NASA does not currently have a training design that supports semi-autonomous to autonomous mission operations. The current design of a manned Mars mission calls for a 32-month long mission and asynchronous communication involving long delays.

Barshi, Immanuel↗

NASA NTRS2018

Field Camp for Astronauts: NASA's Geoscience Training Program for Planetary Exploration

Fifty years ago Apollo astronauts walked on the Moon to explore the geology and collect samples for Earth return. Several authors have discussed the strategic planning and training that enabled the Apollo successes, and assembled recommendations regarding today’s lunar science objectives and astronaut training required to achieve those science goals. Since the 1980s, geoscience training for astronauts focused on observing the Earth from orbit. Today, we are building a geoscience training program to support informed Earth observations as well as the exploration culture for future human missions to the Moon and Mars. Our team partnered with JSC’s crew training and astronaut offices to develop our 4-week geoscience program for the 2017 astronaut class. Because the astronauts have a variety of professional backgrounds, we provide a broad introduction to Earth and planetary sciences. But our prime focus is 2 weeks of intensive field work, a methodology introduced with the 2013 astronaut class. We completed the first half of the training – a field trip to observe hurricane deposits along Galveston Bay; keynotes by Apollo colleagues highlighting Apollo experiences; a tightly-integrated week of introductory geology in the classroom followed by a week of fieldwork in the Rio Grande del Norte National Monument. The classroom included interactive map exercises that allowed the students to progressively build a base map of the field area that they used as a starting point for their week-long mapping exercise. We divided the class into small mapping groups to conduct their observations, mapping and interpretation of the geology. In addition to learning geological field work, our field training provided the platform for practicing expeditionary leadership, a key skill set valued by NASA for astronaut crews. Next summer the capstone fieldwork for the 2017 astronauts will include both mapping and rock sampling. Throughout the mapping, the class will collect additional data to help inform field and sampling decisions using diagnostic field instruments that are being tested in analog settings for their operational efficacy for future planetary exploration.

Evans, Cynthia A.↗

NASA NTRS2019

Motion Cueing for Stall Recovery Training in Commercial Transport Simulators

Starting in 2019, airline pilots will be required to perform full stall recovery training in flight simulators. Historically, training simulators weren't required to provide training at conditions outside their normal flight envelope. Post-stall aircraft models are generally required to be implemented to simulate the aircraft response after the stall point. In addition, motion cues need to adequately represent this response to ensure the skills learned in simulator training are directly usable in real flight. This paper provides and overview of six simulator experiments conducted at NASA Ames Research Center to develop a motion cueing strategies for stall recovery training in commercial transport simulators. One of the experiments verified an enhanced motion cueing strategy for stall recovery training on a level-D-certified full flight simulator. This study showed that the enhanced motion results in lower maximum roll angles in the stall maneuver, lower minimum load factors in the recovery, lower numbers of secondary stick shakers in the stall recovery, and a lower maximum airspeed in the recovery. These results indicate that relatively minor enhancements to the motion logic of heritage commercial transport simulators can significantly improve pilot performance in simulated stall recoveries, and potentially improve stall recovery training.

stall recovery↗

NASA NTRS2019

Motion Cueing for Stall Recovery Training in Commercial Transport Simulators

stall recovery↗

NASA NTRS2019

Assessing ISS Habitability Training: A Historical Perspective

Over the lifetime of the International Space Station (ISS) training evolved based on Astronaut feedback, system updates, increased mission duration, and shifting operational goals. This training is a mixture of skill-based instruction and mission specific lessons on many topics. Habitability training specifically focuses on preparing astronauts for the day-to-day needs of living in space such as hygiene, food, sleep, restraints/mobility aids, and housekeeping. In this paper, the authors evaluate the effectiveness of habitability training for Astronaut working on the International Space Station (ISS) over the course of its continuously manned operation since 2000. This involved identifying ISS habitability training changes in lessons objectives and skills between revisions. These identified changes were then compared with relevant Astronaut feedback from post mission debriefs to determine the effectiveness of the changes. Habitability training is essential for Astronauts and has changed and continues to evolve over the course of the ISS. Understanding ISS habitability and how to most effectively train it will generate lessons learned that benefit current ISS operations and future exploration outside of Low Earth Orbit (LEO) to the moon and eventually Mars.

Swarmer, Tiffany M.↗

NASA NTRS2019

Training for Long-Duration Space Missions: A Literature Review into Skill Retention and Generalizatility

In long-duration space missions, skill retention and generalizability become ever more important as mission length increases, for it is through these capabilities that astronaut crews achieve autonomy. A framework is proposed for future research based on three types of training commonly found in operational scenarios: initial training, refresher training, and just-in-time training. One of the key points of interest identified from the body of literature is the need for a structured and quantifiable approach to characterize skill decay. Such an approach would allow gaining a deeper understanding of the mechanisms through which skill decay operates. Because simulators are used extensively in all types of training, the effects of simulator fidelity on skill retention and generalizability are paramount to understand. Some common fidelity types are defined and their effects on the human operator are discussed. From a review of the literature on all these topics, several research gaps were identified that led to three key opportunities for future research: (1) developing skill decay functions provides theoretical insights into skill decay and allows for several practical applications, such as planning refresher training, (2) the effects of simulator fidelity on skill decay functions should be investigated empirically, and (3) investigating the generalizability of skills learned in initial training to other tasks could provide space crews with greater autonomy. A cybernetic approach might give new insights.

Pieters, Marc A.↗

NASA NTRS2021

Practical Operational Readiness Gambits: Operations Training Simulations for the Curiosity Rover

The Mars Science Laboratory team had been puttingin effort to make a Training Venue to allow for parallel shadowtactical operations for trainees to actively work alongside theprime tactical operations personnel without affecting operations,since staffing constraints and shortened operations timelineswere straining the tactical process in supporting operationstrainees in the traditional way. The COVID- 19 Pandemic presentedfurther challenges in continuing on-console training forMSL operations trainees. The MSL operations team switched tofully remote operations, thus hampering the direct mentorshipa trainee would normally receive while on site at JPL. TheMars 2020 training team had developed a concept for roveroperations training simulations based on Johnson Space Center’sextensive simulations training program for astronauts andflight controllers. The MSL team borrowed this idea, and implementedthese training simulations which are called PracticalOperational Readiness Gambits (PORGs). The PORGs so farhave focused on the Science Planner and Rover Planner roles,which are two crucial roles in tactical operations that engagein key interactions throughout a shift. PORGs are based onactual sol scenarios that have occurred on Mars and follow thetactical operations process and timeline as closely as possible.However, unlike an operations shift, PORGs can slow down orpause to allow for more mentoring time. PORGs can focus onparticular skills to test the trainees on their understanding ofa concept. PORGs increase in complexity with each scenario toease the trainees into more typical tactical operations workloads.As more trainees join the MSL operations team, more roles arebeing incorporated into PORGs. There are plans to incorporatecertified operations personnel into PORGs to practice anomalyresponse situations. PORGs have become an essential part of theMSL operations training program and will continue even afterthe return to on-site operations at JPL.

Gajeway, Jocelyn↗

NASA NTRSDate not supplied

Game Based Learning For Earth Science Applications Training

Current NASA Earth capacity development programs employ mechanisms ranging from online resource sharing, and virtual and in-person trainings to share knowledge. While these programs are highly successful at engaging individuals around the world – in 2018, over 8000 individuals and over 2000 institutions from all 50 US states and over 140 countries were engaged through over 150 projects and trainings – user feedback has highlighted the desire for expanded hands-on, practical experiences in incorporating NASA EO insights with localized data and actions. We aim to address this gap by leveraging the benefits of game-based learning to build user skills in integrating NASA and local EO data to guide decisions for climate resiliency and hazard planning. This project is being executed as a two-phase crowdsourced challenge: 1) Phase 1 will require a well-researched product concept that reflects an understanding of NASA’s Earth data and tools and user needs, and proposes an innovative and interactive game or extended reality experience to train users in identifying relevant NASA data and applying insights to their climate resiliency decisions; 2) Winners of Phase 1 will be provided seed funding to develop a working prototype of the product. We aim to award 1-3 final winners to support the development of more than one game, thereby ensuring that NASA's diverse audiences around the world can access training games that best suit their needs and capabilities. This EO training game project fits in the NASA Earth Science Applied Sciences Program’s Capacity Development Program, contributing to the program mission of “helping people around the world better understand [NASA’s Earth] data and find ways to use them” (https://appliedsciences.nasa.gov/what-we-do/capacity-building). The final training game will complement existing programmatic activities of workforce development, trainings, and collaborative projects, while providing the unique value of providing interactive experiences to users and collecting real-time data and feedback to improve NASA’s Earth applications’ products and services related to climate resilience.

Human centered design↗

NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗

NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗

NASA NTRSDate not supplied

Isolator Shock Train Response to Dual-Mode Scramjet Throttling-Like Forcing

Typical dual-mode scramjet designs employ an isolator component situated between the inlet and combustor. The isolator fluidically buffers the inlet from the combustor via the formation of a shock train, an interconnected series of shock-boundary layer interactions. The isolator is required during ramjet-engine operation and at the lower edges of the scramjet operational envelope. In an operational engine, transient behavior imposes fluidic dynamics due to attitude adjustments, combustor throttling, aberrant resonances within the combustor, etc. To accommodate these transients, the shock train adjusts on both temporal and spatial scales. Sufficiently large downstream pressure increases in the combustor play a critical role during unstart, whereby strong compression waves propagate upstream in the isolator to ultimately force the shock train out the inlet, if it cannot stabilize to the pressure increase. This critically reduces the engine mass flow rate, potentially causing a catastrophic reduction in thrust and loss of the vehicle. A nearly instantaneous unstart can, in principle, be predicted by a ratio of the normal shock pressure jump in an inviscid isolator. However, lower-magnitude perturbations, below this bound, also induce unstarts. Understanding the transient behavior inherent in unsteady isolator operation is critical to reducing safety margin in isolator length and refining engine control algorithms to improve engine performance. The Isolator Dynamics Research Lab (IDRL) is a cold-flow, direct-connect isolator facility ideal for assessing the fundamental fluid dynamics of shock trains. The internal flow of a scramjet isolator is modeled using a 2D, symmetric Mach 2.5 nozzle followed by a 695 mm long, 25.4x50.8 mm rectangular isolator test section. A blockage cone on a high-speed ball screw actuator downstream of the isolator is pulsed to simulate the throttling behavior in an operational combustor. Wall flush high-speed static pressure transducers detect the streamwise shock train within the isolator. Time delays between the onset of the throttling and the response motion of the shock train are analyzed to assess the controlling scales and parameters. From this analysis, the response time of the isolator shock train system is found to depend primarily on the isolator back pressure time-rate-of-change but quickly saturates to a maximum propagation speed along the isolator. These results inform dual-mode scramjet engine designers of the critical parameters and limits of an unstart resistant engine.

scramjet↗

NASA NTRSDate not supplied

Artemis Crewed Surface Operations Training Dust Mitigation Test/Training Facility (DMTF)

Based on several Apollo mission(s) reports and lessons learned1 on lunar dust impacts, it is imperative for successful Artemis missions, to develop dust mitigation methods & train crew in dust cleaning techniques for efficient use of lunar surface time and for crew health. Longer durations and increased number of Extra Vehicular Activities (EVAs) for Artemis need better training. Apollo missions lacked training in a dust facility. Current facilities do not provide adequate fidelity to train crew in dust cleaning operations in suited, pressurized environments. Mimicking dust cleaning actions in existing facilities (eg- Neutral Buoyancy lab) does not provide in depth understanding of actions and time needed to clean suit or positions crew need to take to remove dust from suit. Available dust bins do not support suited/pressurized ops, personnel not trained in suited ops, more overhead/travel. A local facility at Johnson Space Center (JSC) w/ adequate fidelity supporting suited/pressurized ops, personnel in suited ops is a crucial need for rapid development/testing and training. This facility will provide NASA capabilities to: 1. Provide a dust containment bin to perform suited pressurized crewed training/testing activities for dust interaction/mitigation leveraging existing JSC facility infrastructure and 2. Perform end-to-end simulated lunar dust testing of fluid, mechanical, and electrical systems to provide “test like you fly” confidence for industry developed solutions.

EVA training↗

NASA NTRS1993

Effect of exercise training on cardiopulmonary baroreflex control of forearm vascular resistance in humans

We studied the stimulus-response characteristics of cardiopulmonary baroreflex control of forearm vascular resistance (FVR) in four groups of male volunteer subjects: i) unfit, ii) physically fit, iii) before and after 10 wk of endurance training (chronic blood volume expansion), and iv) before and after acute blood volume expansion. We assessed the relationship between reflex stimulus, i.e., changes in central venous pressure and response, i.e., FVR, during unloading of cardiopulmonary mechanoreceptors with lower body negative pressure (LBNP, 0 to -20 mm Hg). The slope of the linear relationship between FVR and CVP, the index of the responsiveness of this baroreflex, was significantly diminished (> 50%) in the fit subjects compared with the unfit. The slope of the FVR-CVP relationship was inversely correlated with the subject's total blood volume, suggesting that blood volume expansion was related to the attenuated CP baroreflex. In the exercise training study, maximal oxygen consumption and blood volume increased following 10 wk of endurance training (N = 14) but were unchanged in the time control group (N = 7). The slope of the FVR-CVP relationship was significantly reduced (32%) following 10 wk of training but was unchanged in the time control group. The reduction in slope of the FVR-CVP relationship was inversely related to the increase in blood volume associated with exercise training. Acute blood volume expansion 8 ml.kg-1 body weight with 5% human serum albumin solution) significantly reduced the slope of the FVR-CVP relationship. These data support the hypothesis that the attenuated forearm vascular reflex in physically fit individuals is related to a training-induced hypervolemia.(ABSTRACT TRUNCATED AT 250 WORDS).

Review↗

NASA NTRS2017

Training, Retention, and Transfer of Data Entry Perceptual and Motoric Processes Over Long Retention Intervals

Subjects trained in a standard data entry task, which involved typing numbers (e.g., 5421) using their right hands. At test (6 months post-training), subjects completed the standard task, followed by a left-hand variant (typing with their left hands) that involved the same perceptual, but different motoric, processes as the standard task. At a second test (8 months post-training), subjects completed the standard task, followed by a code variant (translating letters into digits, then typing the digits with their right hands) that involved different perceptual, but the same motoric, processes as the standard task. For each of the three tasks, half the trials were trained numbers (old) and half were new. Repetition priming (faster response times to old than new numbers) was found for each task. Repetition priming for the standard task reflects retention of trained numbers; for the left-hand variant reflects transfer of perceptual processes; and for the code variant reflects transfer of motoric processes. There was thus evidence for both specificity and generalizability of training data entry perceptual and motoric processes over very long retention intervals.

dat↗