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NASA NTRS1972

Space simulation experiments on reaction control system thruster plumes.

A space simulation procedure was developed for studying rocket plume contamination effects using a 5-lb bipropellant reaction control system thrustor. Vacuum chamber pressures of 0.00003 torr (70 miles altitude) were achieved with the thrustor firing in pulse trains consisting of eight pulses - 50 msec on, 100 msec off, and seven minutes between pulse trains. The final vacuum was achieved by cooling all vacuum chamber surfaces to liquid-helium temperature and by introducing a continuous argon leak of 48 std. cc/sec into the test chamber. Fast time response liquid flow rate measurements showed that large variations occurred in the ratio of oxidizer to fuel flow for pulse-on times up to 120 msec. These variations could lead to poor combustion efficiency and the production of contamination.

Cassidy, J. F.↗

NASA NTRS2018

A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high dimensional input space of the training samples into a low dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications. Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum. Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map. In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach.

Initialization method↗

NASA NTRSDate not supplied

Preliminary Design of an 'Autonomous Medical Response Agent' Interface Prototype for Long Duration Spaceflight

Major challenges for astronauts in future long-duration exploration missions (LDEMs) will be that crewmembers are not expected to be medical professionals, may be under high workload and stress, are facing physiological challenges caused by spaceflight, and will have limited, delayed voice communications with medical support from Earth. An autonomous medical response agent (AMRA) is envisioned to help astronauts address medical complaints, develop a differential diagnosis, and guide self-treatment until a healthy state is restored. AMRA develops a process of personalized diagnosis and treatment through a Bayesian predictive control system that recommends therapeutic control actions including diagnostic tests and treatments to crewmembers (Menon, 2020). The Human Computer Interaction (HCI) lab from NASA Ames Research Center’s Human Systems Integration Division (Code TH) has collaborated with Nahlia Inc in human-centered design augmentation research for AMRA. The project, titled Design of ‘Autonomous Medical Response Agent Interface Prototype for Long Duration Spaceflight, has been funded by the Translational Research Institute for Space Health (TRISH) and introduces an interactive user-interface prototype that guides astronauts through self-diagnosis, treatment, and rehabilitation while communicating with remote specialists in ground support (most notably a patient’s flight surgeon). Our project develops the interaction design for the crewmember using AMRA through user research, iterative design, and usability testing to evaluate the user interface and workflow designed. The interface design deliverable for this project, titled AMRA Aggregate Information Display (AMRA AID) is an integrated information display system for comprehensive autonomous medical guidance, diagnosis, and treatment of in-flight medical conditions experienced by crewmembers. AMRA AID demonstrates how we might ensure crew autonomy, increase the crew’s medical capabilities, and decrease cognitive burden within a front-end user interface. AMRA AID refrains from relying on input from ground or mission control for self-treatment of medical issues—though ground awareness and communication with ground is maintained as a means of ensuring trust between mission control and crew. AMRA AID demonstrates how the crew’s on-board medical system might integrate with information from vehicle monitoring and crew schedule, without assuming causal relationships. AMRA AID’s comprehensive view enables efficient information access for both crew and ground support, reducing cognitive burden in the event of an unplanned or emergency medical incident and enabling informed analytical decisions to be made based on both crew and vehicle health. Human-centered design augmentation advanced within the prototype included: enhanced workflow and treatment guidance for two medical scenarios for a non-specialist user base with various levels of medical training, interaction design which considered speech (conversational user interface) elements and on-screen interactions to be developed in future iterations of the project, communication design and functional requirements relevant to self-care versus caring for another astronaut, as well as user testing of the prototype with an international space medical community. This project arrives at critical findings regarding usability needs, communication requirements, and integrated information requirements for a future technology interface functioning to increase confidence between ground support and LDEM crewmembers.

TRISH↗

NASA NTRS2017

Beyond Control Centers

For NASA's Resource Prospector (RP) Lunar Rover Mission, we are moving away from a control center concept, to a fully distributed operation utilizing control nodes, with decision support from anywhere via mobile devices. This operations concept will utilize distributed information systems, notifications, mobile data access, and optimized mobile data display for off-console decision support. We see this concept of operations as a step in the evolution of mission operations from a central control center concept to a mission operations anywhere concept. The RP example is part of a trend, in which mission expertise for design, development and operations is distributed across countries and across the globe. Future spacecraft operations will be most cost efficient and flexible by following this distributed expertise, enabling operations from anywhere. For the RP mission we arrived at the decision to utilize a fully distributed operations team, where everyone operates from their home institution, based on evaluating the following factors: the requirement for physical proximity for near-real time command and control decisions; the cost of distributed control nodes vs. a centralized control center; the impact on training and mission preparation of flying the team to a central location. Physical proximity for operational decisions is seldom required, though certain categories of decisions, such as launch abort, or close coordination for mission or safety-critical near-real-time command and control decisions may benefit from co-location. The cost of facilities and operational infrastructure has not been found to be a driving factor for location in our studies. Mission training and preparation benefit from having all operators train and operate from home institutions.

Operations↗

NASA NTRS2011

Human Factors Throughout the Life Cycle: Lessons Learned from the Shuttle Program

With the ending of the Space Shuttle Program, it is critical that we not forget the Human Factors lessons we have learned over the years. At every phase of the life cycle, from manufacturing, processing and integrating vehicle and payload, to launch, flight operations, mission control and landing, hundreds of teams have worked together to achieve mission success in one of the most complex, high-risk socio-technical enterprises ever designed. Just as there was great diversity in the types of operations performed at every stage, there was a myriad of human factors that could further complicate these human systems. A single mishap or close call could point to issues at the individual level (perceptual or workload limitations, training, fatigue, human error susceptibilities), the task level (design of tools, procedures and aspects of the workplace), as well as the organizational level (appropriate resources, safety policies, information access and communication channels). While we have often had to learn through human mistakes and technological failures, we have also begun to understand how to design human systems in which individuals can excel, where tasks and procedures are not only safe but efficient, and how organizations can foster a proactive approach to managing risk and supporting human enterprises. Panelists will talk about their experiences as they relate human factors to a particular phase of the shuttle life cycle. They will conclude with a framework for tying together human factors lessons-learned into system-level risk management strategies.

human factors↗

NASA NTRSDate not supplied

Training the Powered-Lift Evaluation Pilot

This poster describes a project to prepare pilots for a study assessing novel aircraft automation concepts for electric Vertical Takeoff and Landing (eVTOL) aircraft using NASA’s Vertical Motion Simulator (VMS). By exploring the operational and learning challenges related to transitioning between forward flight and vertical landing, we seek to establish baselines of pilot workload and aircraft handling qualities across varying atmospheric conditions and automation states. The simulated eVTOL design differentiates flight control allocations as a function of airspeed across four speed ranges as the vehicle transitions between fully thrust-borne lift and wing-borne lift. As speed increases, side stick controls command: translational ground speeds, vertical and lateral acceleration, vertical rate, vertical flight path angle, and bank angle. This novel approach to flight control allocation creates a significant learning challenge for pilots. Since initial eVTOL aircraft may have limitations on hover capabilities, automation and flight guidance cues also vary with airspeed to provide efficient landing profiles while still providing cues suitable for cruise flight. The NASA team prepared the study pilots to follow these flight guidance cues along curved Required Navigation Performance (RNP) approaches and along 6o and 12o glide paths to energy-efficient assistive-hover landing and goarounds. The pre-VMS preparation sought to prepare pilots from diverse levels of experience and background. To do this, NASA researchers designed and developed a fixed-based, large field-ofview simulator with terrain, structures, and air traffic. With one day of combined classroom learning and skill development in the fixedbase simulator, pilots were largely able to fly the simulated eVTOL in the VMS with sufficient mastery to provide handling quality assessments using the Cooper-Harper Handling Qualities Rating and workload assessments through the Bedford Workload Scale.

AAM↗

NASA NTRS1996

Feasibility Study of a Rotorcraft Health and Usage Monitoring System (HUMS): Results of Operator's Evaluation

The objective was to evaluate the feasibility of a state-of-the-art health and usage monitoring system (HUMS) to provide monitoring of critical mechanical systems on the helicopter, including motors, drive train, engines, and life-limited components. The implementation of HUMS and cost integration with current maintenance procedures was assessed from the operator's viewpoint in order to achieve expected benefits from these systems, such as enhanced safety, reduced maintenance cost, and increased availability. An operational HUMS that was installed and operated under an independent flight trial program was used as a basis for this study. The HUMS equipment and software were commercially available. Based on the results of the feasibility study, the HUMS used in the flight trial program generally demonstrated a high level of reliability in monitoring the rotor system, engines, drive train, and life-limited components. The system acted as a sentinel to warn of impending failures. A worn tail rotor pitch bearing was detected by HUMS, which had the capability for self testing to diagnose system and sensor faults. Examples of potential payback to the operator with HUMS were identified, including reduced insurance cost through enhanced safety, lower operating costs derived from maintenance credits, increased aircraft availability, and improved operating efficiency. The interfacing of HUMS with current operational procedures was assessed to require only minimal revisions to the operator's maintenance manuals. Finally the success in realizing the potential benefits from HUMS technology was found to depend on the operator, helicopter manufacturer, regulator (FAA), and HUMS supplier working together.

Romero, Raylund↗

NASA NTRS1995

Displacement based multilevel structural optimization

Multidisciplinary design optimization (MDO) is expected to play a major role in the competitive transportation industries of tomorrow, i.e., in the design of aircraft and spacecraft, of high speed trains, boats, and automobiles. All of these vehicles require maximum performance at minimum weight to keep fuel consumption low and conserve resources. Here, MDO can deliver mathematically based design tools to create systems with optimum performance subject to the constraints of disciplines such as structures, aerodynamics, controls, etc. Although some applications of MDO are beginning to surface, the key to a widespread use of this technology lies in the improvement of its efficiency. This aspect is investigated here for the MDO subset of structural optimization, i.e., for the weight minimization of a given structure under size, strength, and displacement constraints. Specifically, finite element based multilevel optimization of structures (here, statically indeterminate trusses and beams for proof of concept) is performed. In the system level optimization, the design variables are the coefficients of assumed displacement functions, and the load unbalance resulting from the solution of the stiffness equations is minimized. Constraints are placed on the deflection amplitudes and the weight of the structure. In the subsystems level optimizations, the weight of each element is minimized under the action of stress constraints, with the cross sectional dimensions as design variables. This approach is expected to prove very efficient, especially for complex structures, since the design task is broken down into a large number of small and efficiently handled subtasks, each with only a small number of variables. This partitioning will also allow for the use of parallel computing, first, by sending the system and subsystems level computations to two different processors, ultimately, by performing all subsystems level optimizations in a massively parallel manner on separate processors. It is expected that the subsystems level optimizations can be further improved through the use of controlled growth, a method which reduces an optimization to a more efficient analysis with only a slight degradation in accuracy. The efficiency of all proposed techniques is being evaluated relative to the performance of the standard single level optimization approach where the complete structure is weight minimized under the action of all given constraints by one processor and to the performance of simultaneous analysis and design which combines analysis and optimization into a single step. It is expected that the present approach can be expanded to include additional structural constraints (buckling, free and forced vibration, etc.) or other disciplines (passive and active controls, aerodynamics, etc.) for true MDO.

Striz, Alfred G.↗

NASA NTRS2005

Use of New Communication Technologies to Change NASA Safety Culture: Incorporating the Use of Blogs as a Fundamental Communications Tool

The purpose of this paper is to explore an innovative approach to culture change at NASA that goes beyond reorganizations, management training, and a renewed emphasis on safety. Over the last five years, a technological social revolution has been emerging from the internet. Blogs (aka web logs) are transforming traditional communication and information sharing outlets away from established information sources such as the media. The Blogosphere has grown from zero blogs in 1999 to approximately 4.5 million as of November 2004 and is expected to double in 2005. Blogs have demonstrated incredible effectiveness and efficiency with regards to affecting major military and political events. Consequently, NASA should embrace the new information paradigm presented by blogging. NASA can derive exceptional benefits from the new technology as follows: 1) Personal blogs can overcome the silent safety culture by giving voice to concerns or questions that are not well understood or seemingly inconsequential to the NASA community at-large without the pressure of formally raising a potential false alarm. Since blogs can be open to Agency-wide participation, an incredible amount of resources from an extensive pool of experience can focus on a single issue, concern, or problem and quickly vetted, discussed and assessed for feasibility, significance, and criticality. The speed for which this could be obtained cannot be matched through any other process or procedure currently in use. 2) Through official NASA established blogs, lessons learned can be a real-time two way process that is formed and implemented from the ground level. Data mining of official NASA blogs and personal blogs of NASA personnel can identify hot button issues and concerns to senior management. 3) NASA blogs could function as a natural ombudsman for the NASA community. Through the recognition of issues being voiced by the community and taking a proactive stance on those issues, credibility within NASA Management can be restored. For NASA to harness the capabilities of blogs, NASA must develop an Agency-wide policy on blogging to encourage use and provide guidance. This policy should describe basic rules of conduct and content as well as a policy of non-retribution and/or anonymity. The Agency must provide sever space within their firewalls, provide appropriate software tools, and promote blogs in newsletters and official websites. By embracing the use of blogs, a potential pool of 19,000 experts could be available to address each posted safety issue, concern, problem, or question. Blogs could result in real NASA culture change.

Huls, Dale thomas↗

NASA NTRS2005

E-Nose Vapor Identification Based on Dempster-Shafer Fusion of Multiple Classifiers

Electronic nose (e-nose) vapor identification is an efficient approach to monitor air contaminants in space stations and shuttles in order to ensure the health and safety of astronauts. Data preprocessing (measurement denoising and feature extraction) and pattern classification are important components of an e-nose system. In this paper, a wavelet-based denoising method is applied to filter the noisy sensor measurements. Transient-state features are then extracted from the denoised sensor measurements, and are used to train multiple classifiers such as multi-layer perceptions (MLP), support vector machines (SVM), k nearest neighbor (KNN), and Parzen classifier. The Dempster-Shafer (DS) technique is used at the end to fuse the results of the multiple classifiers to get the final classification. Experimental analysis based on real vapor data shows that the wavelet denoising method can remove both random noise and outliers successfully, and the classification rate can be improved by using classifier fusion.

Li, Winston↗

NASA NTRS1998

Neural Networks for Rapid Design and Analysis

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Sparks, Dean W., Jr.↗

NASA NTRS2021

Mapping Global Forest Age from Forest Inventories, Biomass and Climate Data

Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. However, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. This study provides a new global distribution of forest age circa 2010, estimated using a machine learning approach trained with more than 40 000 plots using forest inventory, biomass and climate data. First, an evaluation against the plot-level measurements of forest age reveals that the data-driven method has a relatively good predictive capacity of classifying old-growth vs. non-old-growth (precision = 0.81 and 0.99 for old-growth and non-old-growth, respectively) forests and estimating corresponding forest age estimates (NSE = 0.6 – Nash–Sutcliffe efficiency – and RMSE = 50 years – root-mean-square error). However, there are systematic biases of overestimation in young- and underestimation in old-forest stands, respectively. Globally, we find a large variability in forest age with the old-growth forests in the tropical regions of Amazon and Congo, young forests in China, and intermediate stands in Europe. Furthermore, we find that the regions with high rates of deforestation or forest degradation (e.g. the arc of deforestation in the Amazon) are composed mainly of younger stands. Assessment of forest age in the climate space shows that the old forests are either in cold and dry regions or warm and wet regions, while young–intermediate forests span a large climatic gradient. Finally, comparing the presented forest age estimates with a series of regional products reveals differences rooted in different approaches and different in situ observations and global-scale products. Despite showing robustness in cross-validation results, additional methodological insights on further developments should as much as possible harmonize data across the different approaches. The forest age dataset presented here provides additional insights into the global distribution of forest age to better understand the global dynamics in the forest water and carbon cycles. The forest age datasets are openly available at https://doi.org/10.17871/ForestAgeBGI.2021 (Besnard et al., 2021).

Simon Besnard↗

NASA NTRSDate not supplied

Contextualizing Air Traffic Management Conversations using Natural Language Understanding

Efficient management of air traffic and mitigation of delays depend on extracting actionable information from unstructured data, such as dialogues from the Federal Aviation Administration’s (FAA’s) Air Traffic Control System Command Center (ATCSCC) telecons. This study presents a pipeline utilizing Natural Language Processing (NLP) methods for Intent Classification (IC) and Slot Filling (SF) to identify and extract Traffic Management Initiatives (TMIs) from aviation-specific dialogues. We leveraged DeBERTa, a pre-trained transformer model, and fine-tuned it to the nuances of the aviation domain. Despite challenges posed by annotation complexities, the IC model achieved promising results with a weighted average F1-score of 0.81. Our results are close to those of human annotators, which demonstrates the model’s strong alignment with human-level performance. The SF model also showed strong performance, achieving a weighted F1-score of 0.97, which demonstrates its effectiveness in accurately predicting key slots. Our analysis revealed limitations in handling less frequent intents and slot labels due to data sparsity, motivating future efforts to adopt joint IC-SF modeling and data augmentation strategies. This research highlights the potential of domain-specific NLP to streamline decision-making in the aviation industry and improve the management of TMIs.

Air Traffic Control Management↗

NASA NTRSDate not supplied

Surface Systems and Interface Standardization

Space exploration on planetary surfaces will require the use of various surface systems which will likely need to interface with one another. These systems must communicate and share data, as well as distribute power and transfer fluids for sustainable surface operations. Such systems can range from landers, surface habitats, mobility systems, cargo, and In-Situ Resource Utilizations (ISRUs). Previous and current programs have developed space interoperability standards that help aid in reducing potential risks of interface integration. One example of this is the International Deep Space Interoperability Standards (IDSIS) which focuses on deep space exploration. A goal of the Artemis program is to develop a sustained human presence on the lunar surface that would be a training ground for future Mars exploration. Therefore, it will be beneficial to identify interface standards between the surface assets which will help mitigate risk and reduce complexity in the harsh environments of space exploration. A key contribution to sustainability is commonality between hardware and software interfaces. Generic interfaces for data, power, and fluids will reduce risk, promote interoperability, and define standard interfaces across surface exploration projects and programs. Standardized interfaces would be advantageous for improving efficiency and reducing overall complexity, which are critical considerations for future space exploration. Furthermore, it will provide cost reductions to the Artemis programs over their life cycle (in Operations & Maintenance (O&M) and Logistics). Unique proprietary interfaces if considered or allowed would not only increase complexity but also add cost to the programs. Of course, understanding what will work and not work effectively in these unique environments such as the lunar surface is important. These unique environments require much needed intelligent design, prototyping, comprehensive testing, and field experience, utilizing consensus on common interface solutions.

Jaime Gomez↗

NASA NTRS1993

Efficient fault diagnosis of helicopter gearboxes

Application of a diagnostic system to a helicopter gearbox is presented. The diagnostic system is a nonparametric pattern classifier that uses a multi-valued influence matrix (MVIM) as its diagnostic model and benefits from a fast learning algorithm that enables it to estimate its diagnostic model from a small number of measurement-fault data. To test this diagnostic system, vibration measurements were collected from a helicopter gearbox test stand during accelerated fatigue tests and at various fault instances. The diagnostic results indicate that the MVIM system can accurately detect and diagnose various gearbox faults so long as they are included in training.

Chin, H.↗

NASA NTRS2018

TCL2 National Campaign Human Factors Brief

The Technology Capability Level-2 National Campaign (TCL2nc) was conducted at six different test-sites located across the USA, during May and June of 2017. The campaign resulted in over 240 data collection flights using 24 different aircraft and involving 23 flight crews. Flights not only varied in duration, but also in the environments and terrains over which they flew. The TCL2nc highlighted beyond visual line of sight (BVLOS) and altitude-stratified operations, and saw five partners bring their own, independently built, UAS Service Supplier (USS) for use during the flight tests. This document presents data collected during the TCL2nc that informs the 'Operator' section of the 'Requirements/Best Practices' from the UTM Technical Capability Matrix and Guidelines to Operate (Rios, version as of March 2017). A review of the data collected indicated that although teams were well qualified on paper (in terms of both completing training and having experience with flying UAS vehicles), greater consideration should be given to the unique perspectives and backgrounds of future UAS operators. Overall, teams looked at a variety of sources for information, including USS client-displays, and participants became more mindful of the need to be aware of other vehicles, highlighting the value of reporting information. Observations found that flight crews' time to respond to a UTM issue depended heavily on the team structure, communication efficiency, and crew procedures. These points are discussed in more detail below.

unmanned vehicles↗

NASA NTRS2017

NASA's UTM Research

The Technology Capability Level-2 National Campaign (TCL2nc) was conducted at six different test sites located across the USA, during May and June of 2017. The campaign resulted in over 250 data collection flights using 26 different aircraft and involving 23 flight crews. Flights not only varied in duration, but also in the environments and terrains over which they flew. The TCL2nc highlighted beyond visual line of sight (BVLOS) and altitude-stratified operations, and saw five partners bring their own, independently built, UTM Service Supplier (USS) for use during the flight tests. This document presents data collected during the TCL2nc that informs the 'Operator' section of the 'Requirements/Best Practices' from the UTM Technical Capability Matrix and Guidelines to Operate (Rios, version as of March 2017). A review of the data collected indicated that although teams were well qualified on paper (in terms of both completing training and having experience with flying UAS vehicles), greater consideration should be given to the unique perspectives and backgrounds of future UAS operators. Overall, teams looked at a variety of sources for information, including USS client-displays, and participants became more aware of the need to be aware of other vehicles, highlighting the value of reporting information. Observations found that flight crews' time to respond to a UTM issue depended heavily on the team structure, communication efficiency, and crew procedures.

Mercer, Joey↗

NASA NTRS2014

Retrospective Analysis of Inflight Exercise Loading and Physiological Outcomes

Astronauts perform exercise throughout their missions to counter the health declines that occur as a result of long-term exposure to weightlessness. Although all astronauts perform exercise during their missions, the specific prescriptions, and thus the mechanical loading, differs among individuals. For example, inflight ground reaction force data indicate that subject-specific differences exist in foot forces created when exercising on the second-generation treadmill (T2) [1]. The current exercise devices allow astronauts to complete prescriptions at higher intensities, resulting in greater benefits with increased efficiency. Although physiological outcomes have improved, the specific factors related to the increased benefits are unknown. In-flight exercise hardware collect data that allows for exploratory analyses to determine if specific performance factors relate to physiological outcomes. These analyses are vital for understanding which components of exercise are most critical for optimal human health and performance. The relationship between exercise performance variables and physiological changes during flight has yet to be fully investigated. Identifying the critical performance variables that relate to improved physiological outcomes is vital for creating current and future exercise prescriptions to optimize astronaut health. The specific aims of this project are: 1) To quantify the exercise-related mechanical loading experienced by crewmembers on T2 and ARED during their mission on ISS; 2) To explore relationships between exercise loading variables, bone, and muscle health changes during the mission; 3) To determine if specific mechanical loading variables are more critical than others in protecting physiology; 4) To develop methodology for operational use in monitoring accumulated training loads during crew exercise programs. This retrospective analysis, which is currently in progress, is being conducted using data from astronauts that have flown long-duration missions onboard the ISS and have had access to exercise on the T2 and the Advanced Resistive Exercise Device (ARED). The specific exercise prescriptions vary for each astronaut. General exercise summary metrics will be developed to quantify exercise intensities, volumes, and durations for each subject. Where available, ground reaction force data will be used to quantify mechanical loading experienced by each astronaut. These inflight exercise metrics will be investigated relative to changes in pre- to post-flight bone and muscle health to identify which specific variables are related with improved or degraded physiological outcomes. The information generated from this analysis will fill gaps related to typical bone loading characterization, exercise performance capability, exercise volume and efficiency, and importance of exercise hardware. In addition, methods for quantification of exercise loading for use in monitoring the exercise programs during future space missions will be explored with the intent to inform exercise scientists and trainers as to the critical aspects of inflight exercise prescriptions.

Ploutz-Snyder, L. L.↗