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NASA NTRS2016

Resource Tracking Model Updates and Trade Studies

The Resource tracking model has been updated to capture system manager and project manager inputs. Both the Trick/GUNNS RTM simulator and the RTM mass balance spreadsheet have been revised to address inputs from system managers and to refine the way mass balance is illustrated. The revisions to the RTM included addition of a Plasma Pyrolysis Assembly (PPA) to recover hydrogen from Sabatier reactor methane which was vented in the prior version of the RTM. The effect of the PPA on the overall balance of resources in an exploration vehicle is illustrated in the increased recycle of vehicle oxygen. Additionally simulation of EVAs conducted from the exploration module was added. Since the focus of the exploration module is to provide a habitat during deep space operations the EVA simulation approach to EVA is based on ISS EVA protocol and processes. Case studies have been run to show the relative effect of performance changes on vehicle resources.

Chambliss, Joe↗

NASA NTRS2016

Experiences with Extra-Vehicular Activities in Response to Critical ISS Contingencies

The maturation of the International Space Station (ISS) design from the proposed Space Station Freedom to today's current implementation resulted in external hardware redundancy vulnerabilities in the final design. Failure to compensate for or respond to these vulnerabilities could put the ISS in a posture where it could no longer function as a habitable space station. In the first years of ISS assembly, these responses were to largely be addressed by the continued resupply and Extra-Vehicular Activity (EVA) capabilities of the Space Shuttle. Even prior to the decision to retire the Space Shuttle, it was realized that ISS needed to have its own capability to be able to rapidly repair or replace external hardware without needing to wait for the next cargo resupply mission. As documented in a previous publication, in 2006 development was started to baseline Extra-Vehicular Activity (EVA, or spacewalk) procedures to replace hardware components whose failure would expose some of the ISS vulnerabilities should a second failure occur. This development work laid the groundwork for the onboard crews and the ground operations and engineering teams to be ready to replace any of this failed hardware. In 2010, this development work was put to the test when one of these pieces of hardware failed. This paper will provide a brief summary of the planning and processes established in the original Contingency EVA development phase. It will then review how those plans and processes were implemented in 2010, highlighting what went well as well as where there were deficiencies between theory and reality. This paper will show that the original approach and analyses, though sound, were not as thorough as they should have been in the realm of planning for next worse failures, for documenting Programmatic approval of key assumptions, and not pursuing sufficient engineering analysis prior to the failure of the hardware. The paper will further highlight the changes made to the Contingency EVA preparation team structure, approach, goals, and the resources allocated to its work after the 2010 events. Finally, the authors will overview the implementation of these updates in addressing failures onboard the ISS in 2012, 2013, and 2014. The successful use of the updated approaches, and the application of the approaches to other spacewalks, will demonstrate the effectiveness of this additional work and make a case for putting significant time and resources into pre-failure planning and analysis for critical hardware items on human-tended spacecraft.

Van Cise, E. A.↗

NASA NTRS1972

Apollo 15 Index of 70 mm Photographs

All Apollo 15 70 mm photographs to which NASA photo numbers have been assigned are described in this index. In the first section all photographs are listed in sequence by NASA photo numbers. In subsequent sections, lunar surface and lunar orbital photographs are cross-indexed. Lunar surface photographs are listed in chronological order within the following categories: SEVA and LM window photos (LM window photos were taken before, between, and after EVA's), EVA 1, EVA 2, and EVA 3. Photographs of the lunar surface taken while in lunar orbit are cross-indexed by longitude in 10 deg increments, starting with the easternmost (farside) photos, and progressing to the westernmost (nearside) photos. Within each 10 deg interval of longitude, photographs are listed sequentially by NASA photo number, not by specific longitude of the principal point of each photograph.

Source record↗

NASA NTRS2016

Automated ISS Flight Utilities

During my internship at NASA Johnson Space Center, I worked in the Space Radiation Analysis Group (SRAG), where I was tasked with a number of projects focused on the automation of tasks and activities related to the operation of the International Space Station (ISS). As I worked on a number of projects, I have written short sections below to give a description for each, followed by more general remarks on the internship experience. My first project is titled "General Exposure Representation EVADOSE", also known as "GEnEVADOSE". This project involved the design and development of a C++/ ROOT framework focused on radiation exposure for extravehicular activity (EVA) planning for the ISS. The utility helps mission managers plan EVAs by displaying information on the cumulative radiation doses that crew will receive during an EVA as a function of the egress time and duration of the activity. SRAG uses a utility called EVADOSE, employing a model of the space radiation environment in low Earth orbit to predict these doses, as while outside the ISS the astronauts will have less shielding from charged particles such as electrons and protons. However, EVADOSE output is cumbersome to work with, and prior to GEnEVADOSE, querying data and producing graphs of ISS trajectories and cumulative doses versus egress time required manual work in Microsoft Excel. GEnEVADOSE automates all this work, reading in EVADOSE output file(s) along with a plaintext file input by the user providing input parameters. GEnEVADOSE will output a text file containing all the necessary dosimetry for each proposed EVA egress time, for each specified EVADOSE file. It also plots cumulative dose versus egress time and the ISS trajectory, and displays all of this information in an auto-generated presentation made in LaTeX. New features have also been added, such as best-case scenarios (egress times corresponding to the least dose), interpolated curves for trajectories, and the ability to query any time in the EVADES output. As mentioned above, GEnEVADOSE makes extensive use of ROOT version 6, the data analysis framework developed at the European Organization for Nuclear Research (CERN), and the code is written to the C++11 standard (as are the other projects). My second project is the Automated Mission Reference Exposure Utility (AMREU).Unlike GEnEVADOSE, AMREU is a combination of three frameworks written in both Python and C++, also making use of ROOT (and PyROOT). Run as a combination of daily and weekly cron jobs, these macros query the SRAG database system to determine the active ISS missions, and query minute-by-minute radiation dose information from ISS-TEPC (Tissue Equivalent Proportional Counter), one of the radiation detectors onboard the ISS. Using this information, AMREU creates a corrected data set of daily radiation doses, addressing situations where TEPC may be offline or locked up by correcting doses for days with less than 95% live time (the total amount time the instrument acquires data) by averaging the past 7 days. As not all errors may be automatically detectable, AMREU also allows for manual corrections, checking an updated plaintext file each time it runs. With the corrected data, AMREU generates cumulative dose plots for each mission, and uses a Python script to generate a flight note file (.docx format) containing these plots, as well as information sections to be filled in and modified by the space weather environment officers with information specific to the week. AMREU is set up to run without requiring any user input, and it automatically archives old flight notes and information files for missions that are no longer active. My other projects involve cleaning up a large data set from the Charged Particle Directional Spectrometer (CPDS), joining together many different data sets in order to clean up information in SRAG SQL databases, and developing other automated utilities for displaying information on active solar regions, that may be used by the space weather environment officers to monitor solar activity. I consulted my mentor Dr. Ryan Rios and Dr. Kerry Lee for project requirements and added features, and ROOT developer Edmond Offermann for advice on using the ROOT library. I also received advice and feedback from Dr. Janet Barzilla of SRAG, who tested my code. Besides these inputs, I worked independently, writing all of the code by myself. The code for all these projects is documented throughout, and I have attempted to write it in a modular format. Assuming that ROOT is updated accordingly, these codes are also Y2038-compliant (and Y10K-compliant). This allows the code to be easily referenced, modified and possibly repurposed for non-ISS missions in the future, should the necessary inputs exist. These projects have taught me a lot about coding and software design - I have become a much more skilled C++ programmer and ROOT user, and I also learned to code in Python and PyROOT (and its advantages and disadvantages compared to C++/ ROOT). Furthermore, I have learned about space radiation and radiation modeling, topics that greatly interest me as I pursue a degree in physics. Working alongside experimental physicists like Dr. Rios, I have developed a greater understanding and appreciation for experimental science, something I have always leaned towards but to which I lacked significant exposure. My work in SRAG has also given me the invaluable opportunity to witness the work environment for physicists at NASA, and what a career in academia may look like at a government laboratory such as NASA Johnson Space Center. As I continue my studies and look forward to graduate school and a future career, this experience at NASA has given me a meaningful and enjoyable opportunity to put my skills to use and see what my future career path might hold.

Offermann, Jan Tuzlic↗

NASA NTRS2017

Integration of an Earth-Based Science Team During Human Exploration of Mars

NASA Extreme Environment Mission Operations (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. A mission was undertaken in 2016, NEEMO 21, at the Aquarius undersea research habitat. During the mission, the effects of varied oper-ations concepts with representative communication latencies as-sociated with Mars missions were studied. Six subjects were weighed out to simulate partial gravity and evaluated different operations concepts for integration and management of a simulated Earth-based science team (ST) who provided input and direction during exploration activities. Exploration traverses were planned in advance based on precursor data collected. Subjects completed science-related tasks including presampling surveys and marine-science-based sampling during saturation dives up to 4 hours in duration that simulated extravehicular activity (EVA) on Mars. A communication latency of 15 minutes in each direction between space and ground was simulated throughout the EVAs. Objective data included task completion times, total EVA time, crew idle time, translation time, ST assimilation time (defined as time available for the science team to discuss, to review and act upon data/imagery after they have been collected and transmitted to the ground). Subjective data included acceptability, simulation quality, capability assessment ratings, and comments. In addition, comments from both the crew and the ST were captured during the post-mission debrief. Here, we focus on the acceptability of the operations concepts studied and the capabilities most enhancing or enabling in the operations concept. The importance and challenges of designing EVA time-lines to account for the length of the task, level of interaction with the ground that is required/desired, and communication latency, are discussed.

Chappell, Steven P.↗

NASA NTRS2017

Application of IRTAM to Support ISS Program Safety

The International Space Station (ISS) orbits near the F-peak of the ionosphere (approximately 400 km altitude). Generally, satellites orbiting at this altitude would have a floating potential (FP) of approximately -1 V due to the electron temperature (Te). However, the ISS has 8 large negatively grounded 160 V solar array wings (SAW) that collect a significant electron current from the ionosphere. This current drives the ISS FP much more negative during insolation and is highly dependent on the electron density (Ne). Also, due to the size of the ISS, magnetic inductance caused by the geomagnetic field produces a delta potential up to 40 V across the truss, possibly producing positive potentials. During Extravehicular Activity (EVA) the negative FP can lead to an arcing hazard when it exceeds -45.5 V, and the positive FP can produce a DC current high enough to stimulate the astronaut's muscles and also cause a hazard. Data collected from the Floating Potential Monitoring Unit (FPMU) have shown that the probability of either of these hazards occurring during times with quiet to moderately disturbed geomagnetic activity is low enough to no longer be considered a risk. However, a study of the ionosphere Ne during severe geomagnetic storm activity has shown that the Ne can be enhanced by a factor of 6 in the ISS orbit. As a result, the ISS Safety Review Panel (SRP) requires that ionospheric conditions be monitored using the FPMU in conjunction with the ISS Plasma Interaction Model (PIM) to determine if a severe geomagnetic storm could result in a plasma environment that could produce a hazard. A 'Real-Time' plasma hazard assessment process was developed to support ISS Program real-time decision making providing constraint relief information for EVAs planning and operations. This process incorporates 'real time' ionospheric conditions, ISS solar arrays' orientation, ISS flight attitude, and where the EVA will be performed on the ISS. This assessment requires real time data that is presently provided by the FPMU including ISS floating potential, along with ionospheric Ne and Te, in order to determine the present environment. Once the present environment conditions are known to be either above, below, or near the current IRI values, the IRI is used to forecast what the environment could become in the event of a severe geomagnetic storm. If the FPMU should fail, the Space Environments team needs another source of data which is utilized to support a short-term forecast for EVAs. The IRI Real-Time Assimilative Mapping (IRTAM) model is an ionospheric model that uses real time measurements from approximately 70 digisondes to produce foF2 and hmF2 global maps in 15 minute cadence. The Boeing Space Environments team has used the IRI coefficients produced in IRTAM to calculate the Ne along the ISS orbital track. The results of the IRTAM model have been compared to FPMU measurements and show excellent agreement (figure 1). IRTAM has been identified as a potential FPMU back-up system will be used as a backup for the FPMU to support the ISS Program following completion of an FPMU/IRTAM validation campaign.

Hartman, William A.↗

NASA NTRS2018

Advanced Multimodal Solutions for Information Presentation

High-workload, fast-paced, and degraded sensory environments are the likeliest candidates to benefit from multimodal information presentation. For example, during EVA (Extra-Vehicular Activity) and telerobotic operations, the sensory restrictions associated with a space environment provide a major challenge to maintaining the situation awareness (SA) required for safe operations. Multimodal displays hold promise to enhance situation awareness and task performance by utilizing different sensory modalities and maximizing their effectiveness based on appropriate interaction between modalities. During EVA, the visual and auditory channels are likely to be the most utilized with tasks such as monitoring the visual environment, attending visual and auditory displays, and maintaining multichannel auditory communications. Previous studies have shown that compared to unimodal displays (spatial auditory or 2D visual), bimodal presentation of information can improve operator performance during simulated extravehicular activity on planetary surfaces for tasks as diverse as orientation, localization or docking, particularly when the visual environment is degraded or workload is increased. Tactile displays offer a third sensory channel that may both offload information processing effort and provide a means to capture attention when urgently required. For example, recent studies suggest that including tactile cues may result in increased orientation and alerting accuracy, improved task response time and decreased workload, as well as provide self-orientation cues in microgravity on the ISS (International Space Station). An important overall issue is that context-dependent factors like task complexity, sensory degradation, peripersonal vs. extrapersonal space operations, workload, experience level, and operator fatigue tend to vary greatly in complex real-world environments and it will be difficult to design a multimodal interface that performs well under all conditions. As a possible solution, adaptive systems have been proposed in which the information presented to the user changes as a function of taskcontext-dependent factors. However, this presupposes that adequate methods for detecting andor predicting such factors are developed. Further, research in adaptive systems for aviation suggests that they can sometimes serve to increase workload and reduce situational awareness. It will be critical to develop multimodal display guidelines that include consideration of smart systems that can select the best display method for a particular contextsituation.The scope of the current work is an analysis of potential multimodal display technologies for long duration missions and, in particular, will focus on their potential role in EVA activities. The review will address multimodal (combined visual, auditory andor tactile) displays investigated by NASA, industry, and DoD (Dept. of Defense). It also considers the need for adaptive information systems to accommodate a variety of operational contexts such as crew status (e.g., fatigue, workload level) and task environment (e.g., EVA, habitat, rover, spacecraft). Current approaches to guidelines and best practices for combining modalities for the most effective information displays are also reviewed. Potential issues in developing interface guidelines for the Exploration Information System (EIS) are briefly considered.

multimodal display↗

NASA NTRS2018

Preparing for Planetary Surface Exploration by Measuring Habitat Dust Intrusion with Filter Tests During an Analogue Mars Mission

As humans venture deeper into space more issues related to operations will become apparent. While the perils ofdust particles may not be widely recognized, it is one of the major issues astronauts will face on the surface of theMoon and Mars. Dust particles present a problem for both astronaut health and equipment as revealed during theApollo era lunar surface missions. Dust particles cling to spacesuits and field gear, which upon ingress would begincirculating throughout the spacecraft or habitat. An astronaut's health is compromised by the dust particle's potentialto embed in the lungs and cause respiratory illnesses. The extreme abrasiveness and granularity of the particles makeit near impossible to completely shield a spacecraft or habitat from dust related damage. NASA's Glenn ResearchCenter collaborated with Crew 188 at the Mars Desert Research Station (MDRS) in Utah to measure how much dustentered the habitat during a series of extravehicular activities (EVAs), or surface excursions. A NASA GRCdeveloped multistage filter system, coined the Scroll Filter System, was tested, for its effectiveness in removing dustthat entered the airlock and habitat after the EVAs. An optical particle counter measured the ambient airlockparticulates five times including: before the start of operations; after the crew left for EVA; in the middle of the EVAwith the settled air; before the crew entered the airlock after EVA; and finally, after the crew simulated repressurizationand suit brushing off in the airlock. Data was also collected in several of the working environmentlocations around MDRS and outside the habitat in the wind. Data collected from this research will help establishfilter equipment for life support systems and prescribed operations for astronaut transition from a planetary surfaceinto a desired clean habitat. Measurements may aid in updating a baseline expected dust load for a surface habitatand further facilitate the mitigation of astronaut's exposure to dust particles on the surface of celestial bodies.

Kobrick, Ryan L.↗

NASA NTRS2019

BioBot: Innovative Offloading of Astronauts for More Effective Exploration

The BioBot concept consists of a robotic rover which is capable of traversing the same terrain as a spacesuited human. It carries the primary life support system for the astronaut, including consumables, atmosphere revitalization systems (e.g., CO2 scrubbing, humidity and temperature management, ventilation fan), power system (e.g., battery, power management and distribution),and thermal control system (e.g., water sublimator, cooling water pump), along with umbilical lines to connect to the supported astronaut. Although not technically part of life support, it would be logical for the BioBot to also provide long-range communications, video monitoring, tool and sample transport, and other functions to enable and enhance EVA productivity in planetary surface exploration.The design reference scenario for this concept is that astronauts involved in future lunar or Mars exploration will be on the surface for weeks or months rather than days, and will be involved in regular EVA operations. It is not unreasonable to think of geologists spending several days inEVA exploration each week over a prolonged mission duration, with far more ambitious operational objectives than were typical of Apollo. In this scenario, each astronaut will be accompanied by a "BioBot", which will transport their life support system and consumables, an extended umbilical and umbilical reel, and robotic systems capable of controlling the position and motion of the umbilical. The astronaut will be connected to the robot via the umbilical, carrying only a small emergency open-loop life support system similar to those contained in every PLSS. The robotic mobility base will be designed to be capable of traveling anywhere the astronaut can walk, and will also be useful as a transport for the EVA tools, science instrumentation, and collected samples. In addition, the BioBot can potentially carry the astronaut on traverses as well. Such a system will also be a significant enhancement to public engagement in these future exploration missions, as the robotic vehicles can also support high-resolution cameras and high bandwidth communications gear to providehigh-definition video coverage of each crew throughout each EVA sortie.

Akin, David L.↗

NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. This was expected to accelerate development and provide more cost-effective, time-saving solutions. This work was selected for a NASA Crowdsourcing project through an agency-wide solicitation. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation and an execution crowdsourcing platform partner to solicit framework developments from external contenders. NASA provided contenders with video clips of spacesuits and simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy (weighted combination of scoring metrics). Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA simulation environments such as the Neutral Buoyancy Lab (NBL). However, 3D joint identification is less reliable when parts of the suit were obstructed in the image. After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗

NASA NTRSDate not supplied

Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE) – Physical Workload Approximation

The Human Physiology, Performance, Protection & Operations Laboratory (H-3PO) at NASA Johnson Space Center (JSC) is developing a hybrid reality exploration surface analog, “Assessments of Physiology and Cognition in Hybrid-reality Environments” (APACHE). The goal of APACHE is to create a planetary extravehicular activity (EVA) simulation environment that provides a representative physical and cognitive workload approximation using a combination of virtual reality (VR), physical reality, and hybrid reality (HR) techniques. To develop and characterize the physical workload approximation within the APACHE environment, a two-part approach was implemented. In part 1, baseline physical work load during ambulation within APACHE was evaluated and compared with that in other planetary EVA analog environments and with existing data sets from Apollo planetary EVAs and reduced gravity testing of prototype planetary spacesuits. For this evaluation, 10 subjects were asked to ambulate in three surface analog environments: a passive treadmill in APACHE, natural terrain in an outdoor field environment, and a standard motorized treadmill. Subjects’ heart rate and metabolic rate (VO2/VCO2) were measured and compared among the different test conditions and existing data sets. Gait parameters were also collected to compare with suited mechanics and to understand the role of gait kinematics in physical workload. In part 2, the aim is to evaluate the addition of a custom weighted body suit to the aforementioned surface analog environments and the ability to titrate the suit configuration to provide the best possible physical workload approximation for simulation of lunar and Martian EVAs.

Alex Baughman↗

NASA NTRSDate not supplied

ISS External Microorganisms: A Payload to Close Planetary Protection Knowledge Gaps for Crewed Missions

Before NASA or COSPAR is able to set planetary protection requirements for crewed missions to locations like Mars there are a number of critical knowledge gaps that must be addressed (1). One of the most important knowledge gaps is an understanding of microbial leakage from crewed habitats and space suits. Current ECLSS (Environmental Control and Life Support System) and PLSS (Portable Life Support System) requirements do not include any provisions to control microbes that may escape along with vented or leaked gasses. The current generation of NASA space suits can leak at rates as high as 100 cm2 /min. during nominal operation (2). ISS (International Space Station) intentionally vents atmospheric gases like CO2 to maintain habitable conditions for the crew. Furthermore, every time an airlock is used for EVA (extravehicular activity)there is an accompanying release of internal atmosphere. Since it is not possible to sterilize a crewed mission, it is important that we understand what if any microbes are entrained in these vented and leaked products. It is also important to understand if these microbes can survive on exterior surfaces. Recent sampling of the Russian segments of ISS suggest that bacteria and fungi from inside ISS may be capable of surviving on external surfaces(3). NASA is developing an aseptic sampling tool for use during EVA and plans to collect samples from vents on ISS to build on these results. The results of this work will be used to develop planetary protection requirements for vented and leaked gasses from crewed volumes. NASA has developed and tested a tool kit for collecting microbiological samples during EVA(4). This tool kit contains eight commercially available, 23 mm. diameter, foam swabs that can be used to aseptically collect samples while at vacuum. The swabs are individually housed in aluminum canisters that are equipped with 0.2 μm Teflon filters. These filters allow the canisters to equilibrate to pressure changes while preventing microbiological contamination. The canisters will be cleaned and sterilized before flight. Results from ground-based testing indicate that this tool kit is capable of aseptically collecting microbes while at vacuum without becoming contaminated during pressure changes(5). Based on the results of this ground testing we have modified the tool kit to meet NASA safety requirements and improve the ergonomics. We added additional mounting points to the tool kit to give astronauts more options for securing it during use. We also changed the opening mechanism to improve the precision with which swabs can be extracted from the tool kit. We plan to use this kit on an upcoming EVA to collect samples from non-propulsive vents and areas near the U.S. airlock on ISS. These samples will be frozen at -80 ̊C and stored on station until they can be returned to Earth. We will analyze these returned samples using next generation DNA sequencing to determine the community composition and function of external ISS environments. The results of this study will close planetary protection knowledge gaps for crewed missions and will help NASA determine appropriate planetary protection requirements for life support systems. The tool kit will also be useful for collecting aseptic samples on upcoming crewed or robotic missions and could easily be modified to collect samples with organic contamination control requirements as well.

A B Regberg↗

NASA NTRSDate not supplied

Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE) – Physical Workload Approximation

The Human Physiology, Performance, Protection & Operations Laboratory (H-3PO) at NASA Johnson Space Center (JSC) is developing a hybrid reality exploration surface analog, “Assessments of Physiology And Cognition in Hybrid-reality Environments” (APACHE). The goal of APACHE is to create a planetary extravehicular activity (EVA) simulation environment that provides a representative physical and cognitive workload approximation using a combination of virtual reality (VR), physical reality, and hybrid reality (HR) techniques. To develop and characterize the physical workload approximation within the APACHE environment, a two-part approach was implemented. In part 1, baseline physical workload during ambulation within APACHE was evaluated and compared with that in other planetary EVA analog environments and with existing data sets from Apollo planetary EVAs and reduced gravity testing of prototype planetary spacesuits. For this evaluation, 10 subjects were asked to ambulate in three surface analog environments: a passive treadmill in APACHE, natural terrain in an outdoor field environment, and a standard motorized treadmill. Subjects’ heart rate and metabolic rate (VO2/VCO2)were measured and compared among the different test conditions and existing data sets. Gait parameters were also collected to compare with suited mechanics and to understand the role of gait kinematics in physical workload. In part 2, the aim is to evaluate the addition of a custom weighted body suit to the aforementioned surface analog environments and the ability to titrate the suit configuration to provide the best possible physical workload approximation for simulation of lunar and Martian EVAs.

Alexander J Baughman↗

NASA NTRSDate not supplied

Modeling Spacesuit-Human Interaction for Injury Risk Identification

Performing functional tasks while inside of the space suit introduces additional ergonomic challenges which can lead to musculoskeletal stresses and injuries in astronauts. Computational modeling of the suit-body interaction can be used alongside human-in-the-loop testing to understand and mitigate these risks and potentially improve the work performance. In this work, a static rigid-body model of the human body was integrated with the space suit and the process is described. This model enables prediction of the resulting joint torques and musculoskeletal loading from a simulated extravehicular activity (EVA) task. For representative EVA tasks, joint torque and body angles were used as parameters to predict the muscle strength required to achieve the task, as well as the percentage of the general and astronaut-like population that could achieve that strength demand. Population strength capability can be used as an indicator of the difficulty of a task and the level of injury risk for the given task types and crew anthropometry. The sensitivity of the model to inputs such as body joint location within the space suit and individual anthropometry will also be evaluated. Future work will use a database of3D human body scans and the movements of the body inside the suit during different EVA tasks to identify potential soft tissue contact points. The specific locations and magnitudes of contacts between the body and suit are expected to identify risk of repetitive tissue contact stresses. Overall, this model will provide a new tool to structurally identify the injury risks of EVA tasks and evaluate alternate strategies to reduce injury risk.

Garima Gupta↗

NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Preliminary work into this field was promising but given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation (CoCEI) and an execution crowdsourcing platform partner to solicit machine learning framework developments from external contenders. NASA provided contenders with images and video clips of spacesuits with simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, the top five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The weighted scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy. Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA environments such as the NASA Active Response Gravity Offload System (ARGOS). After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗

NASA NTRSDate not supplied

ISS External Microorganisms: A Payload to Close Planetary Protection Knowledge Gaps for Crewed Missions

A B Regberg↗

NASA NTRSDate not supplied

Effect of Carbon Dioxide Exposure on Physical and Cognitive Performance in a Simulated Spaceflight Contingency Scenario

Introduction: Carbon dioxide (CO2) produced by astronauts inside space suits can accumulate to levels that affect health and performance. The human health and performance risks associated with different levels of CO2 exposure in the flight environment remain an area of debate. The purpose of this study is to characterize the limit of acceptable performance (cognitive and physical) decrements and symptom severity for mission operations when subjected to elevated inspired CO2 levels in the spacesuit during contingency EVA scenarios. Methods: This study will create a simulation of a 1-hour contingency EVA walk back to the habitat, incorporating a passive treadmill and a fully immersive virtual reality (VR) simulation of a lunar EVA. Subjects will be asked to walk on a treadmill while breathing partial pressures of CO2 of 0, 5, 10, 15, 20, 25, 30mmHg for 1 hour at a time. Cognitive performance will be quantified using validated cognitive tests and measures of functional task performance embedded within the VR environment. Test subject symptoms and self-assessment of performance will be evaluated via survey. Results: This study currently has approval from NASA’s Institutional Review Board and has completed the Test Readiness Review process. Next steps for this research study include test subject recruitment, data collection, and analysis. Data collection is planned for calendar year 2022 and 2023. Discussion: This study will provide valuable information regarding how various partial pressures of CO2 exposure impact acute health, as well as cognitive and physical performance during simulated lunar EVA. This information will be vital in the assessment of overall risk associated with current hardware (vehicle and suit) design for upcoming exploration missions. It will also be invaluable for informing future standards and requirements.

carbon dioxide↗

NASA NTRSDate not supplied

Medical Lessons Learned from the Exploration Atmospheres Study

Background: The National Aeronautics and Space Administration’s (NASA) Exploration Atmospheres study (EA) was done to evaluate alternative cabin atmospheres for future spacecraft designs and planetary surface exploration of the Moon, Mars and beyond. Deep space exploration involves creating habitats and environments safe for human occupancy and means to explore the outside environment (extravehicular activities, EVA). In order to validate alternative atmospheres and pre-spacewalk procedures, the EA study was conducted to evaluate factors such as hypoxia risks, denitrogenation protocols, food limitations, medications, and the impact of other factors on human performance. Overview: Space travel is constrained by mass, volume, power and the cost of vehicle development, which creates tradeoffs in various capabilities, including breathing gasses. Higher atmospheric pressure in a vehicle means more gas, and a stronger containment vessel/habitat; while lower pressure requires higher oxygen partial pressure, which may increase fire risk. NASA’s EA study evaluated a proposed alternative cabin environment (8.2 psia, 34% Oxygen), for future spacecraft habitat, and planetary EVAs. EA included both a 3-day and a 11-day trial. These trials included a depressurization and saturation to 8.2psi at 34% O2 with additional depresses to 4.3 psia at 85% O2 for simulated EVAs, (1 EVA during the 3-day and 5 during the 11-day trials). Discussion: Planning for and executing the medical monitoring and response plan for a trial of this scope was a huge undertaking with no prior practice to fall back on. Food obstacles, sleeping issues, medications, joint injury, equipment limitations, medical privacy, multiple cases of decompression sickness, and even a COVID outbreak among the support team proved challenging. Conclusion: Testing of this nature is an essential part NASA’s preparation for the upcoming Lunar Artemis missions. As spaceflight transitions beyond low earth orbit, to planetary, even more trials of this nature will be required to learn what are the optimal atmospheric and associated operational constraints to maintain the optimal health of the crew and achieve mission objectives.

R Sanders↗