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NASA NTRS2019

Utilization of Unsupervised Anomalies Detector as a Tool for Managing the TDRS Constellation at GSFC

NASA’s Goddard Space Flight Center (GSFC) operates a constellation of ten geosynchronous Tracking and Data Relay Satellites (TDRS). The mission of the TDRS constellation is to provide relay communications from low-earth orbiting spacecraft to the primary ground station at the White Sands Complex in Las Cruces, New Mexico. Major customers include the International Space Station and Hubble Space Telescope. The NASA Space Network project office at GSFC manages the constellation of spacecraft. The constellation is over 30 years old, and a wide range of technologies and manufacturing techniques are represented on-orbit. Since 1983, the TDRS constellation has recorded thousands of gigabytes of telemetry data. Spacecraft telemetry data has changed throughout the three generations of TDRS spacecraft, however each spacecraft has the same basic functions with some generational enhancements. The constellation includes several spacecraft that have significantly outlived the manufacturer's projected lifetime. This has provided NASA with a significant benefit in terms of return on investment, however it places a burden on efficient management of the assets for maximum life without permitting a TDRS spacecraft to become stranded in its geosynchronous orbital slot. Consequently, the highest level of attention is paid to systems whose failure could strand a TDRS spacecraft in orbit. In this paper, we proposed two stages of analyzing spacecraft anomalies using data mining (DM) to enhance on-going predictions of spacecraft life, subsystem performance, and analysis of subsystem anomalies. The first stage conducts the unsupervised anomaly detector to detect potential anomalies in real-time telemetry data. The second stage introduced telemetry weight (TW) to each telemetry parameter to determine which parameter caused the strongest anomaly. We will present case studies of some of these analyses and how the data can impact decisions on the management of the constellation.

Ma, Kenneth Y.↗

NASA NTRS2015

NASA Software Cost Estimation Model: An Analogy Based Estimation Model

The cost estimation of software development activities is increasingly critical for large scale integrated projects such as those at DOD and NASA especially as the software systems become larger and more complex. As an example MSL (Mars Scientific Laboratory) developed at the Jet Propulsion Laboratory launched with over 2 million lines of code making it the largest robotic spacecraft ever flown (Based on the size of the software). Software development activities are also notorious for their cost growth, with NASA flight software averaging over 50% cost growth. All across the agency, estimators and analysts are increasingly being tasked to develop reliable cost estimates in support of program planning and execution. While there has been extensive work on improving parametric methods there is very little focus on the use of models based on analogy and clustering algorithms. In this paper we summarize our findings on effort/cost model estimation and model development based on ten years of software effort estimation research using data mining and machine learning methods to develop estimation models based on analogy and clustering. The NASA Software Cost Model performance is evaluated by comparing it to COCOMO II, linear regression, and K- nearest neighbor prediction model performance on the same data set.

Hihn, Jairus↗

NASA NTRS2016

EVA Wiki - Transforming Knowledge Management for EVA Flight Controllers and Instructors

The EVA Wiki was recently implemented as the primary knowledge database to retain critical knowledge and skills in the EVA Operations group at NASA's Johnson Space Center by ensuring that information is recorded in a common, easy to search repository. Prior to the EVA Wiki, information required for EVA flight controllers and instructors was scattered across different sources, including multiple file share directories, SharePoint, individual computers, and paper archives. Many documents were outdated, and data was often difficult to find and distribute. In 2011, a team recognized that these knowledge management problems could be solved by creating an EVA Wiki using MediaWiki, a free and open-source software developed by the Wikimedia Foundation. The EVA Wiki developed into an EVA-specific Wikipedia on an internal NASA server. While the technical implementation of the wiki had many challenges, one of the biggest hurdles came from a cultural shift. Like many enterprise organizations, the EVA Operations group was accustomed to hierarchical data structures and individually-owned documents. Instead of sorting files into various folders, the wiki searches content. Rather than having a single document owner, the wiki harmonized the efforts of many contributors and established an automated revision controlled system. As the group adapted to the wiki, the usefulness of this single portal for information became apparent. It transformed into a useful data mining tool for EVA flight controllers and instructors, as well as hundreds of others that support EVA. Program managers, engineers, astronauts, flight directors, and flight controllers in differing disciplines now have an easier-to-use, searchable system to find EVA data. This paper presents the benefits the EVA Wiki has brought to NASA's EVA community, as well as the cultural challenges it had to overcome.

Johnston, Stephanie S.↗

NASA NTRS2016

EVA Wiki - Transforming Knowledge Management for EVA Flight Controllers and Instructors

The EVA (Extravehicular Activity) Wiki was recently implemented as the primary knowledge database to retain critical knowledge and skills in the EVA Operations group at NASA's Johnson Space Center by ensuring that information is recorded in a common, searchable repository. Prior to the EVA Wiki, information required for EVA flight controllers and instructors was scattered across different sources, including multiple file share directories, SharePoint, individual computers, and paper archives. Many documents were outdated, and data was often difficult to find and distribute. In 2011, a team recognized that these knowledge management problems could be solved by creating an EVA Wiki using MediaWiki, a free and open-source software developed by the Wikimedia Foundation. The EVA Wiki developed into an EVA-specific Wikipedia on an internal NASA server. While the technical implementation of the wiki had many challenges, the one of the biggest hurdles came from a cultural shift. Like many enterprise organizations, the EVA Operations group was accustomed to hierarchical data structures and individually-owned documents. Instead of sorting files into various folders, the wiki searches content. Rather than having a single document owner, the wiki harmonized the efforts of many contributors and established an automated revision control system. As the group adapted to the wiki, the usefulness of this single portal for information became apparent. It transformed into a useful data mining tool for EVA flight controllers and instructors, and also for hundreds of other NASA and contract employees. Program managers, engineers, astronauts, flight directors, and flight controllers in differing disciplines now have an easier-to-use, searchable system to find EVA data. This paper presents the benefits the EVA Wiki has brought to NASA's EVA community, as well as the cultural challenges it had to overcome.

Johnston, Stephanie↗

NASA NTRS2016

EVA Wiki - Transforming Knowledge Management for EVA Flight Controllers and Instructors

The EVA Wiki was recently implemented as the primary knowledge database to retain critical knowledge and skills in the EVA Operations group at NASA's Johnson Space Center by ensuring that information is recorded in a common, easy to search repository. Prior to the EVA Wiki, information required for EVA flight controllers and instructors was scattered across different sources, including multiple file share directories, SharePoint, individual computers, and paper archives. Many documents were outdated, and data was often difficult to find and distribute. In 2011, a team recognized that these knowledge management problems could be solved by creating an EVA Wiki using MediaWiki, a free and open-source software developed by the Wikimedia Foundation. The EVA Wiki developed into an EVA-specific Wikipedia on an internal NASA server. While the technical implementation of the wiki had many challenges, one of the biggest hurdles came from a cultural shift. Like many enterprise organizations, the EVA Operations group was accustomed to hierarchical data structures and individually-owned documents. Instead of sorting files into various folders, the wiki searches content. Rather than having a single document owner, the wiki harmonized the efforts of many contributors and established an automated revision controlled system. As the group adapted to the wiki, the usefulness of this single portal for information became apparent. It transformed into a useful data mining tool for EVA flight controllers and instructors, as well as hundreds of others that support the EVA. Program managers, engineers, astronauts, flight directors, and flight controllers in differing disciplines now have an easier-to-use, searchable system to find EVA data. This paper presents the benefits the EVA Wiki has brought to NASA's EVA community, as well as the cultural challenges it had to overcome.

Johnston, Stephanie S.↗

NASA NTRS2013

Space Weather Impacts to Conjunction Assessment: A NASA Robotic Orbital Safety Perspective

National Aeronautics and Space Administration (NASA) recognizes the risk of on-orbit collisions from other satellites and debris objects and has instituted a process to identify and react to close approaches. The charter of the NASA Robotic Conjunction Assessment Risk Analysis (CARA) task is to protect NASA robotic (unmanned) assets from threats posed by other space objects. Monitoring for potential collisions requires formulating close-approach predictions a week or more in the future to determine analyze, and respond to orbital conjunction events of interest. These predictions require propagation of the latest state vector and covariance assuming a predicted atmospheric density and ballistic coefficient. Any differences between the predicted drag used for propagation and the actual drag experienced by the space objects can potentially affect the conjunction event. Therefore, the space environment itself, in particular how space weather impacts atmospheric drag, is an essential element to understand in order effectively to assess the risk of conjunction events. The focus of this research is to develop a better understanding of the impact of space weather on conjunction assessment activities: both accurately determining the current risk and assessing how that risk may change under dynamic space weather conditions. We are engaged in a data-- ]mining exercise to corroborate whether or not observed changes in a conjunction event's dynamics appear consistent with space weather changes and are interested in developing a framework to respond appropriately to uncertainty in predicted space weather. In particular, we use historical conjunction event data products to search for dynamical effects on satellite orbits from changing atmospheric drag. Increased drag is expected to lower the satellite specific energy and will result in the satellite's being 'later' than expected, which can affect satellite conjunctions in a number of ways depending on the two satellites' orbits and the geometry of the conjunction. These satellite time offsets can form the basis of a new technique under development to determine whether space weather perturbations, such as coronal mass ejections, are likely to increase, decrease, or have a neutral effect on the collision risk due to a particular close approach.

Ghrist, Richard↗

NASA NTRS2014

Robust Informatics Infrastructure Required For ICME: Combining Virtual and Experimental Data

With the increased emphasis on reducing the cost and time to market of new materials, the need for robust automated materials information management system(s) enabling sophisticated data mining tools is increasing, as evidenced by the emphasis on Integrated Computational Materials Engineering (ICME) and the recent establishment of the Materials Genome Initiative (MGI). This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Further, the use of increasingly sophisticated nonlinear, anisotropic and or multi-scale models requires both the processing of large volumes of test data and complex materials data necessary to establish processing-microstructure-property-performance relationships. Fortunately, material information management systems have kept pace with the growing user demands and evolved to enable: (i) the capture of both point wise data and full spectra of raw data curves, (ii) data management functions such as access, version, and quality controls;(iii) a wide range of data import, export and analysis capabilities; (iv) data pedigree traceability mechanisms; (v) data searching, reporting and viewing tools; and (vi) access to the information via a wide range of interfaces. This paper discusses key principles for the development of a robust materials information management system to enable the connections at various length scales to be made between experimental data and corresponding multiscale modeling toolsets to enable ICME. In particular, NASA Glenn's efforts towards establishing such a database for capturing constitutive modeling behavior for both monolithic and composites materials

Mutli-scale models↗

NASA NTRS2003

Living with a Star Space Environment Testbed

Summary of activities: (1) FYO1 NRA - Model development and data mining. (2) FY03 NRA - Flight investigations. (3) SET carrier development. (4) Study for accommodation of SET carrier to support advanced detectors. (5) Collaboration with other programs: LWS TR&T to maximize synergy between TR&T space environment research and SET space environment effects research. LWS Data System to optimize dissemination of SET data. NASA Electronic Parts and Packaging Program to leverage ground testing of technologies. Defense Threat Reduction Agency to leverage ground testing and common interests in advanced detectors. and Air Force Research Laboratory to leverage flight opportunities. (6) Education and Public Outreach.

Barth, Janet↗

NASA NTRS2004

Value-added Data Services at the Goddard Earth Sciences Data and Information Services Center

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), in addition to serving the Earth Science community as one of the major Distributed Active Archives Centers (DAACs), provides much more than just data. Among the value-added services available to general users are subsetting data spatially and/or by parameter, online analysis (to avoid downloading unnecessarily all the data), and assistance in obtaining data from other centers. Services available to data producers and high-volume users include consulting on building new products with standard formats and metadata and construction of data management systems. A particularly useful service is data processing at the DISC (i.e., close to the input data) with the users algorithm. This can take a number of different forms: as a configuration-managed algorithm within the main processing stream; as a stand-alone program next to the on-line data storage; as build-it-yourself code within the Near-Archive Data Mining (NADM) system; or as an on-the-fly analysis with simple algorithms embedded into the web-based tools. Partnerships between the GES DISC and scientists, both producers and users, allow the scientists to concentrate on science, while the GES DISC handles the data management, e.g., formats, integration, and data processing. The existing data management infrastructure at the GES DISC supports a wide spectrum of options: from simple data support to sophisticated on-line analysis tools, producing economies of scale and rapid time-to-deploy. At the same time, such partnerships allow the GES DISC to serve the user community more efficiently and to better prioritize on-line holdings. Several examples of successful partnerships are described in the presentation.

Leptoukh, Gregory G.↗

NASA NTRS2005

An Ensemble Approach to Building Mercer Kernels with Prior Information

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Srivastava, Ashok N.↗

NASA NTRS2004

Putting Priors in Mixture Density Mercer Kernels

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

Srivastava, Ashok N.↗

NASA NTRS2012

An Integrated Approach for Gear Health Prognostics

In this paper, an integrated approach for gear health prognostics using particle filters is presented. The presented method effectively addresses the issues in applying particle filters to gear health prognostics by integrating several new components into a particle filter: (1) data mining based techniques to effectively define the degradation state transition and measurement functions using a one-dimensional health index obtained by whitening transform; (2) an unbiased l-step ahead RUL estimator updated with measurement errors. The feasibility of the presented prognostics method is validated using data from a spiral bevel gear case study.

He, David↗

NASA NTRS2009

Software Tools Streamline Project Management

Three innovative software inventions from Ames Research Center (NETMARK, Program Management Tool, and Query-Based Document Management) are finding their way into NASA missions as well as industry applications. The first, NETMARK, is a program that enables integrated searching of data stored in a variety of databases and documents, meaning that users no longer have to look in several places for related information. NETMARK allows users to search and query information across all of these sources in one step. This cross-cutting capability in information analysis has exponentially reduced the amount of time needed to mine data from days or weeks to mere seconds. NETMARK has been used widely throughout NASA, enabling this automatic integration of information across many documents and databases. NASA projects that use NETMARK include the internal reporting system and project performance dashboard, Erasmus, NASA s enterprise management tool, which enhances organizational collaboration and information sharing through document routing and review; the Integrated Financial Management Program; International Space Station Knowledge Management; Mishap and Anomaly Information Reporting System; and management of the Mars Exploration Rovers. Approximately $1 billion worth of NASA s projects are currently managed using Program Management Tool (PMT), which is based on NETMARK. PMT is a comprehensive, Web-enabled application tool used to assist program and project managers within NASA enterprises in monitoring, disseminating, and tracking the progress of program and project milestones and other relevant resources. The PMT consists of an integrated knowledge repository built upon advanced enterprise-wide database integration techniques and the latest Web-enabled technologies. The current system is in a pilot operational mode allowing users to automatically manage, track, define, update, and view customizable milestone objectives and goals. The third software invention, Query-Based Document Management (QBDM) is a tool that enables content or context searches, either simple or hierarchical, across a variety of databases. The system enables users to specify notification subscriptions where they associate "contexts of interest" and "events of interest" to one or more documents or collection(s) of documents. Based on these subscriptions, users receive notification when the events of interest occur within the contexts of interest for associated document or collection(s) of documents. Users can also associate at least one notification time as part of the notification subscription, with at least one option for the time period of notifications.

Source record↗

NASA NTRS1999

Robotic Rock Classification

This report describes a three-month research program undertook jointly by the Robotics Institute at Carnegie Mellon University and Ames Research Center as part of the Ames' Joint Research Initiative (JRI.) The work was conducted at the Ames Research Center by Mr. Liam Pedersen, a graduate student in the CMU Ph.D. program in Robotics under the supervision Dr. Ted Roush at the Space Science Division of the Ames Research Center from May 15 1999 to August 15, 1999. Dr. Martial Hebert is Mr. Pedersen's research adviser at CMU and is Principal Investigator of this Grant. The goal of this project is to investigate and implement methods suitable for a robotic rover to autonomously identify rocks and minerals in its vicinity, and to statistically characterize the local geological environment. Although primary sensors for these tasks are a reflection spectrometer and color camera, the goal is to create a framework under which data from multiple sensors, and multiple readings on the same object, can be combined in a principled manner. Furthermore, it is envisioned that knowledge of the local area, either a priori or gathered by the robot, will be used to improve classification accuracy. The key results obtained during this project are: The continuation of the development of a rock classifier; development of theoretical statistical methods; development of methods for evaluating and selecting sensors; and experimentation with data mining techniques on the Ames spectral library. The results of this work are being applied at CMU, in particular in the context of the Winter 99 Antarctica expedition in which the classification techniques will be used on the Nomad robot. Conversely, the software developed based on those techniques will continue to be made available to NASA Ames and the data collected from the Nomad experiments will also be made available.

Hebert, Martial↗

NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗

NASA NTRS2011

Space Motion Sickness - Analysis of Medical Debriefs Data for Incidence and Treatment

Astronauts use medications for the treatment of a variety of illnesses during space travel. Data mining efforts to assess minor clinical conditions occurring during Shuttle flights STS-1 through STS-94 revealed that space motion sickness (SMS) was the most common ailment during early flight days, occurring in approx.40% of crewmembers, followed by digestive system disturbances (9%) and infectious diseases, which most commonly involved the respiratory or urinary tracts. A more recent analysis of postflight medical debriefs data to examine trends with respect to medication use by astronauts during spaceflights indicated that ~37% of all prescriptions recorded was for pain followed by sleep (22%), SMS (18%), decongestion (14%), and all others (14%). Further analysis revealed that about 150 of 317 crewmembers experienced symptoms of SMS. Nearly all (132 of 150) crewmembers took medication for the treatment of symptoms with a total of 387 doses. Promethazine was taken most often (201 doses); in most cases this resulted in alleviation of symptoms with 130 crewmembers (65%) reporting feeling much or somewhat better. Although fewer total doses of the combination of promethazine and dextroamphetamine (Phen/Dex) were taken (45 doses), slightly more than half of these doses resulted in improvement. The combination of scopolamine and dextroamphetamine (Scop/Dex) was reported to be effective in only 37% of cases, with 36 of 97 total doses resulting in improvement. A higher percentage (24%) of Scop/Dex doses was reported to be ineffective compared with promethazine alone or as Phen/Dex (10% and 7%, respectively). Comparisons of the effectiveness of the different dosage forms of promethazine revealed that intramuscular injection was most effective in alleviating symptoms with 55% feeling much better, 16% feeling somewhat better, and only 7% feeling no effect or worse. Overall, it appears that promethazine alone was used more frequently during flight and was reported effective for the treatment of SMS.

Putcha, Lakshmi↗

NASA NTRS2001

NASA Exhibits

A series of NASA presentations for the Supercomputing 2001 conference are summarized. The topics include: (1) Mars Surveyor Landing Sites "Collaboratory"; (2) Parallel and Distributed CFD for Unsteady Flows with Moving Overset Grids; (3) IP Multicast for Seamless Support of Remote Science; (4) Consolidated Supercomputing Management Office; (5) Growler: A Component-Based Framework for Distributed/Collaborative Scientific Visualization and Computational Steering; (6) Data Mining on the Information Power Grid (IPG); (7) Debugging on the IPG; (8) Debakey Heart Assist Device: (9) Unsteady Turbopump for Reusable Launch Vehicle; (10) Exploratory Computing Environments Component Framework; (11) OVERSET Computational Fluid Dynamics Tools; (12) Control and Observation in Distributed Environments; (13) Multi-Level Parallelism Scaling on NASA's Origin 1024 CPU System; (14) Computing, Information, & Communications Technology; (15) NAS Grid Benchmarks; (16) IPG: A Large-Scale Distributed Computing and Data Management System; and (17) ILab: Parameter Study Creation and Submission on the IPG.

Deardorff, Glenn↗

NASA NTRS2015

Spacesuit Glove-Induced Hand Trauma and Analysis of Potentially Related Risk Variables

Injuries to the hands are common among astronauts who train for extravehicular activity (EVA). When the gloves are pressurized, they restrict movement and create pressure points during tasks, sometimes resulting in pain, muscle fatigue, abrasions, and occasionally more severe injuries such as onycholysis. Glove injuries, both anecdotal and recorded, have been reported during EVA training and flight persistently through NASA's history regardless of mission or glove model. Theories as to causation such as glove-hand fit are common but often lacking in supporting evidence. Previous statistical analysis has evaluated onycholysis in the context of crew anthropometry only (Opperman et al 2010). The purpose of this study was to analyze all injuries (as documented in the medical records) and available risk factor variables with the goal to determine engineering and operational controls that may reduce hand injuries due to the EVA glove in the future. A literature review and data mining study were conducted between 2012 and 2014. This study included 179 US NASA crew who trained or completed an EVA between 1981 and 2010 (crossing both Shuttle and ISS eras) and wore either the 4000 Series or Phase VI glove during Extravehicular Mobility Unit (EMU) spacesuit EVA training and flight. All injuries recorded in medical records were analyzed in their association to candidate risk factor variables. Those risk factor variables included demographic characteristics, hand anthropometry, glove fit characteristics, and training/EVA characteristics. Utilizing literature, medical records and anecdotal causation comments recorded in crewmember injury data, investigators were able to identify several risk factors associated with increased risk of glove related injuries. Prime among them were smaller hand anthropometry, duration of individual suited exposures, and improper glove-hand fit as calculated by the difference in the anthropometry middle finger length compared to the baseline EVA glove middle finger length.

McFarland, Shane M.↗