Environmental Control and Life Support Systems – Crew Health and Performance – Future Research Needs and Alignment
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Model Based Approaches for Fault Detection, Prognostics, Decision Making in Complex Systems
The presentation discusses application of model based approaches to complex systems. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage.
Physics Informed↗
Hybrid Model Based Approaches for Systems Health Management and Prognostics
To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.
Hybrid Modeling↗
Hybrid Approaches to Systems Health Management and Prognostics
To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.
Systems Health Management↗
Hybrid Model Based Approaches for Systems Health Management and Prognostics
This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.
Prognostics↗
Health and Safety Effects of Airborne Soil Dust in the Americas and Beyond
Risks associated with dust hazards are often underappreciated, a gap between the knowledge pool and public awareness that can be costly for impacted communities. This study reviews the emission sources and chemical, physical, and biological characteristics of airborne soil particles (dust) and their effects on human and environmental health and safety in the Pan-American region. American dust originates from both local sources (western United States, northern Mexico, Peru, Bolivia, Chile, and Argentina) and long-range transport from Africa and Asia. Dust properties, as well as the trends and interactions with criteria air pollutants, are summarized. Human exposure to dust is associated with adverse health effects, including asthma, allergies, fungal infections, and premature death. In the Americas, a well-documented and striking effect of soil dust is its association with Coccidioidomycosis, commonly known as Valley fever, an infection caused by inhalation of soil-dwelling fungi unique to this region. Besides human health, dust affects environmental health through nutrients that increase phytoplankton biomass, contaminants that diminish water supply and affect food (crops/fruits/vegetables and ready-to-eat meat), spread crop and marine pathogens, cause Valley fever among domestic and wild animals, transport heavy metals, radionuclides and microplastics, and reduce solar and wind power generation. Dust is also a safety hazard to road transportation and aviation, in the southwestern US where blowing dust is one of the deadliest weather hazards. To mitigate the harmful effects, coordinated regional and international efforts are needed to enhance dust observations and prediction capabilities, soil conservation measures, and Valley fever and other disease surveillance.
health↗
Human System Risk Management for Space Flight
This brief abstract reviews the development of the current day approach to human system risk management for space flight and the development of the critical components of this process over the past few years. The human system risk management process now provides a comprehensive assessment of each human system risk by design reference mission (DRM) and is evaluated not only for mission success but also for long‐term health impacts for the astronauts. The discipline of bioastronautics is the study of the biological and medical effects of space flight on humans. In 1997, the Space Life Sciences Directorate (SLSD) initiated the Bioastronautics Roadmap (Roadmap) as the "Critical Path Roadmap", and in 1998 participation in the roadmap was expanded to include the National Space Biomedical Research Institute (NSBRI) and the external community. A total of 55 risks and 250 questions were identified and prioritized and in 2000, the Roadmap was base-lined and put under configuration control. The Roadmap took into account several major advisory committee reviews including the Institute of Medicine (IOM) "Safe Passage: Astronaut care for Exploration Missions", 2001. Subsequently, three collaborating organizations at NASA HQ (Chief Health and Medical Officer, Office of Space Flight and Office of Biological & Physical Research), published the Bioastronautics Strategy in 2003, that identified the human as a "critical subsystem of space flight" and noted that "tolerance limits and safe operating bands must be established" to enable human space flight. These offices also requested a review by the IOM of the Roadmap and that review was published in October 2005 as "A Risk Reduction Strategy for Human Exploration of Space: A Review of NASA's Bioastronautics Roadmap", that noted several strengths and weaknesses of the Roadmap and made several recommendations. In parallel with the development of the Roadmap, the Office of the Chief Health and Medical Officer (OCHMO) began a process in 2004 of evaluating the tolerance limits and safe operating bands called for in the Bioastronautics Strategy. Over the next several years, the concept of the "operating bands" were turned into Space Flight Human System Standards (SFHSS), developed by the technical resources of the SLSD at the NASA Johnson Space Center (JSC). These standards were developed and reviewed at the SLSD and then presented to the OCHMO for acceptance. The first set of standards was published in 2007 as the NASA‐STD‐3001, Volume 1, Crew Health that elaborated standards for several physiological areas such as cardiovascular, musculoskeletal, radiation exposure and nutrition. Volume 2, Human Factors, Habitability and Human Health was published in 2011, along with development guidance in the Human Integration Design Handbook (HIDH). Taken together, the SFHSS Volumes 1 and 2, and the HIDH replaced the NASA‐STD‐3000 with new standards and revisions of the older document. Three other changes were also taking place that facilitated the development of the human system risk management approach. In 2005, the life sciences research and development portfolio underwent a comprehensive review through the Exploration Systems Architecture Study (ESAS) that resulted in the reformulation of the Bioastronautics Program into Human Research Program (HRP) that was focused on appropriate mitigation results for high priority human health risks. The baseline HRP budget was established in August 2005. In addition, the OCHMO formulated the Health and Medical Technical Authority (HMTA) in 2006 that established the position of the Chief Medical Officer (CMO) at the NASA JSC along with other key technical disciplines, and the OCHMO became the responsible office for the SFHSS as noted above. The final change was the establishment in 2008 of the Human System Risk Board (HSRB), chaired by the CMO with representation from the HRP, SLSD management and technical experts. The HSRB then began to review all human system risks, established a comprehensive risk management and configuration management plan and data sharing policy. These major developments of standards, the HRP, the HMTA and a forum for review of human system risks (HSRB) facilitated the integration of human research, medical operations, systems engineering and many other disciplines in the comprehensive review of human system risks. The HSRB began a comprehensive review of all potential inflight medical conditions and events and over the course of several reviews consolidated the number of human system risks to 30 where the greatest emphasis is placed for investing program dollars for risk mitigation. The HSRB considers all available evidence from human research, medical operations and occupational surveillance in assessing the risks for appropriate mitigation and future work. All applicable DRMs (low earth orbit 6 and 12 months, deep space sortie for 30 days and 1 year, a one year lunar mission, and a planetary mission for 3 years) are considered as human system risks are modified by the hazards associated with space flight such as microgravity, exposure to radiation, distance from the earth, isolation and a closed environment. Each risk has a summary assessment representing the state of knowledge/evidence base for that risk, the available risk mitigations, traceability to the SFHSS and program requirements, and future work required. These data then can drive coordinated budgets across the HRP, the International Space Station, Crew Health and Safety and Advanced Exploration System budgets. These risk assessments were completed for 6 DRMs in December of 2014 and serve as the baseline for which subsequent research and technology development and crew health care portfolios can be assessed. The HSRB will review each risk at least annually and especially when new information is available that must be considered for effective risk mitigation. The current status of each risk can be reported to program management for operations, budget reviews and general oversight of the human system risk management program.
Davis, Jeffrey↗
Computational Modeling of Space Physiology for Informing Spaceflight Countermeasure Design and Predictions of Efficacy
MOTIVATION: Spaceflight countermeasures mitigate the harmful effects of the space environment on astronaut health and performance. Exercise has historically been used as a countermeasure to physical deconditioning, and additional countermeasures including lower body negative pressure, blood flow occlusion and artificial gravity are being researched as countermeasures to spaceflight-induced fluid shifts. The NASA Digital Astronaut Project uses computational models of physiological systems to inform countermeasure design and to predict countermeasure efficacy.OVERVIEW: Computational modeling supports the development of the exercise devices that will be flown on NASAs new exploration crew vehicles. Biomechanical modeling is used to inform design requirements to ensure that exercises can be properly performed within the volume allocated for exercise and to determine whether the limited mass, volume and power requirements of the devices will affect biomechanical outcomes. Models of muscle atrophy and bone remodeling can predict device efficacy for protecting musculoskeletal health during long-duration missions. A lumped-parameter whole-body model of the fluids within the body, which includes the blood within the cardiovascular system, the cerebral spinal fluid, interstitial fluid and lymphatic system fluid, estimates compartmental changes in pressure and volume due to gravitational changes. These models simulate fluid shift countermeasure effects and predict the associated changes in tissue strain in areas of physiological interest to aid in predicting countermeasure effectiveness. SIGNIFICANCE: Development and testing of spaceflight countermeasure prototypes are resource-intensive efforts. Computational modeling can supplement this process by performing simulations that reduce the amount of necessary experimental testing. Outcomes of the simulations are often important for the definition of design requirements and the identification of factors essential in ensuring countermeasure efficacy.
biomedical data↗
Novel Exercise Hardware Requirements, Development, and Selection Process for Long-Duration Space Flight
Long-duration space flight poses many hazards to the health of the crew. Among those hazards is the physiological deconditioning of the musculoskeletal and cardiovascular systems due to prolonged exposure to microgravity. To combat the physical toll that exploration space flight may take on the crew, NASAs Human Research Program is charged with developing exercise protocols and hardware to maintain astronaut health and fitness during long-term missions. The goal of this effort is to preserve the physical capability of the crew to perform mission critical tasks in transit and during planetary surface operations. As NASA aims toward space travel outside of low-earth orbit (LEO), the constraints placed upon exercise equipment onboard the vehicle increase. Proposed vehicle architectures for transit to and from locations outside of LEO call for limits to equipment volume, mass, and power consumption. While NASA has made great strides in providing for the physical welfare of the crew, the equipment currently used onboard ISS is too large, too massive, and too power hungry to consider for long-duration flight. The goal of the Advanced Exercise Concepts (AEC) project is to maintain the resistive and aerobic capabilities of the current, ISS suite of exercise equipment, while making reductions in size, mass, and power consumption in order to make the equipment suitable for long-duration missions.
physical exercise↗
Human electromagnetic field exposure in wearable communications systems: A review
While wearable technologies have taken essential parts of our daily lives, concerns on human health are often overseen. Mainly due to extreme proximity or direct physical contact to human skin, wearable communications devices are prone to cause higher levels of electromagnetic field (EMF) exposure at human skin surface than any other type of wireless technologies. Nevertheless, while focusing on theoretical aspects of human EMF exposure in wearable communications, prior work paid little to no attention on exact exposure levels generated by commercial wearable devices that are already widely used by public consumers. In this context, this paper provides an extensive review of SAR from various commercial wearable devices that are currently sold in the market, as well as the analytical framework and the current measurement methodologies for standard compliance tests. To extend the discussion to general wearable communications, this paper presents simulation results adopting representative antenna patterns, which leads to suggestion of separation distances to keep the current safety guidelines. In particular, considering the increasing interest in millimeter wave (mmW), this paper sheds light on EMF exposure evaluated at 60 GHz and compares to results from 2.4 GHz.
42 ENGINEERING↗
Environmental health program in NASA
The NASA policy on environmental health uses medical and environmental concepts to: (1) Determine the health status of employees; (2) prevent illness and promote good health among employees; and (3) identify and control factors that affect the health of personnel and quality of environment. Evaluation and control of physical, chemical, radiological and biological factors surrounding personnel and which represent physiological and psychological stresses and impairment are considered.
Marrazzo, R. M.↗
Issues In Space Radiation Protection: Galactic Cosmic Rays
When shielding from cosmic heavy ions, one is faced with limited knowledge about the physical properties and biological responses of these radiations. Herein, the current health is discussed in terms of conventional protection practice and a test biological response model. The impact of biological response on optimum materials selection for cosmic ray shielding is presented in terms of the transmission characteristics of the shield material. Although liquid hydrogen is an optimum shield material, evaluation of the effectiveness of polymeric structural materials must await improvement in our knowledge of both the biological response and the nuclear processes.
Wilson, J. W.↗
Perspective on the impact of weightlessness on calcium and bone metabolism
As humans venture into space to colonize the moon and travel to distant planets in the 21st century, they will be confronted with a bone disease that could potentially limit their space exploration activities or put them at risk for fracture when they return to earth. It is now recognized that an unloading of the skeleton, either due to strict bed rest or in zero gravity, leads on average to a 1%-2% reduction in bone mineral density at selected skeletal sites each month. The mechanism by which unloading of the skeleton results in rapid mobilization of calcium stores from the skeleton is not fully understood, but it is thought to be related to down regulation in PTH and 1,25-dihydroxyvitamin D3 production. Bone modeling and mineralization in chick embryos is not affected by microgravity, suggesting that bone cells adapt and ultimately become addicted to gravity in order to maintain a structurally sound skeleton. Strategies need to be developed to decrease microgravity-induced bone resorption by either mimicking gravity's effect on bone metabolism, or enhancing physically or pharmacologically bone formation in order to preserve astronauts' bone health.
short duration↗
Results From the John Glenn Biomedical Engineering Consortium. A Success Story for NASA and Northeast Ohio
The John Glenn Biomedical Engineering Consortium was established by NASA in 2002 to formulate and implement an integrated, interdisciplinary research program to address risks faced by astronauts during long-duration space missions. The consortium is comprised of a preeminent team of Northeast Ohio institutions that include Case Western Reserve University, the Cleveland Clinic, University Hospitals Case Medical Center, The National Center for Space Exploration Research, and the NASA Glenn Research Center. The John Glenn Biomedical Engineering Consortium research is focused on fluid physics and sensor technology that addresses the critical risks to crew health, safety, and performance. Effectively utilizing the unique skills, capabilities and facilities of the consortium members is also of prime importance. Research efforts were initiated with a general call for proposals to the consortium members. The top proposals were selected for funding through a rigorous, peer review process. The review included participation from NASA's Johnson Space Center, which has programmatic responsibility for NASA's Human Research Program. The projects range in scope from delivery of prototype hardware to applied research that enables future development of advanced technology devices. All of the projects selected for funding have been completed and the results are summarized. Because of the success of the consortium, the member institutions have extended the original agreement to continue this highly effective research collaboration through 2011.
Nall, Marsha M.↗
Overview of NASA Behavioral Health and Performance Standard Measures
NASA’s Human Research Program (HRP) is developing a set of “Standard Measures” for use in spaceflight and spaceflight analog environments to monitor the risks of long-duration missions on human health and performance, including behavioral health, individual and team performance, and social processes. Based on measures selected, developed, and tested under the NASA-funded Behavioral Core Measures project (PI: D.F. Dinges) as well as other projects from NASA’s Human Factors & Behavioral Performance research portfolio, NASA’s Behavioral Health & Performance (BHP) Laboratory is further evaluating the operational feasibility, acceptability, and validity of a multidisciplinary suite of objective, subjective, behavioral, and biological measures for monitoring monitor behavioral health, individual and team performance, and social processes over time. The inaugural generation of the NASA Behavioral Health & Performance (BHP) Standard Measures includes a neurocognitive test battery, actigraphy, physical proximity sensors, cardiovascular monitors, and subjective self-reports of mood, depression, and various team and social processes and performance outcomes.
Roma, P. G.↗
Crew State and Risk Model Development to Predict Hydration Status During Extravehicular Activity Training Events
Introduction: Hydration is critical for optimal human health and performance and dehydration can lead to impaired cardiovascular function, thermal dysregulation, decreased blood plasma volume, and cognitive impacts, particularly during physical activity. Prolonged and repeated extravehicular activities (EVA) without sufficiently available drinking water may increase risk for dehydration, which could impair crew health and impact mission success. Understanding hydration needs and potential effects on health and performance are necessary to optimize crew well-being and enable successful EVA objectives. This study aims to develop a model of hydration status during EVA using water balance techniques. Methods: Water balance measures were collected on 15 healthy astronauts who performed ≈6-hour simulated microgravity extravehicular activity (EVA) training in the NASA Neutral Buoyancy Laboratory (NBL). Data collected included pre-and post-EVA nude body weight (BW), maximum absorption garment (MAG) weight, Disposable In-suit Drink Bag (DIDB) weight, urine specific gravity (USG), and extra pre-EVA intake (W). Variables were combined to create the water balance model as pre-EVA (Hn)= BWn+ MAGn+ DIDBn+ Wnand post EVA (Hn+1) = BWn+1+ MAGn+1+ DIDBn+1. Urine specific gravity values were used to refine water balance measures into hydration categories: Hydrated, Marginally Hydrated, and Dehydrated. Results: Pre-EVA modeling indicated53% of crew were hydrated, 20% were marginally hydrated, and 27% were dehydrated. Alternately, Hn+1 showed 13% of crew remained hydrated, 47% were marginally hydrated, and 40% were dehydrated at the end of the EVA. Furthermore, 75% of the crewmembers who started sufficiently hydrated finished the run marginally hydrated or dehydrated. According to USG indices presented by Casa and Lawrence, et al. (2000), only 25% of the crew started and remained hydrated throughout the EVA, and those who were dehydrated at the outset stayed dehydrated. Conclusion: Model outcomes assessing hydration status during 6-hour simulated microgravity EVAs demonstrate the necessity to further address hydration requirements for optimal human performance during spaceflight and EVA. This study enables additional baseline development of the Crew State and Risk Model Hydration, Nutrition, and Waste Management component that aims to provide individualized crew state and risk predictions during EVAs. Reference: Casa, D. J., Armstrong, L. E., et al. (2000). National Athletic Trainers’ Association Position Statement: Fluid Replacement for Athletes. Journal of Athletic Training, 35:212-224.
L Cooper↗
Determining the amount of physical activity needed for long-term weight control
OBJECTIVE: To evaluate prospectively the influence of habitual physical activity on body weight of men and women and to develop a model that defines the role of physical activity on longitudinal weight change. DESIGN AND SETTING: Occupational cohort study conducted for a mean of 5.5 y. SUBJECTS: A total of 496 (341 male and 155 female) NASA/Johnson Space Center employees who completed the 3 month education component of the employee health-related fitness program and remained involved for a minimum of 2 y. MEASUREMENTS: Body weights were measured at baseline (T1) and follow-up (T2), and habitual physical activity was obtained from the mean of multiple ratings of the 11-point (0-10) NASA Activity Scale (NAS) recorded quarterly between T1 and T2. Other measures included age, gender, VO(2 max) obtained from maximal treadmill testing, body mass index (BMI), and body fat percentage. RESULTS: Multiple regression demonstrated that mean NAS, T1 weight, aging and gender all influence long-term T2 weight. T1 age was significant for the men only. Independently, each increase in mean NAS significantly (P<0.01) reduced T2 weight in men (b=-0.91 kg; 95% CI:-1.4 to-0.42 kg) and women (b=-2.14 kg; 95% CI:-2.93 to-1.35 kg). Mean NAS had a greater effect on T2 weight as T1 weight increased, and the relationship was dose-dependent. CONCLUSIONS: Habitual physical activity is a significant source of long-term weight change. The use of self-reported activity level is helpful in predicting long-term weight changes and may be used by health care professionals when counseling patients about the value of physical activity for weight control.
Exercise↗
Explaining Health Risk Behaviors in the U.S. with Social Deprivation at Local and Regional Levels
Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.
Gokhale, Swapna↗