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All sources NASA NTRS DOE OSTI

DOE OSTI2019

Performance and Compliance Monitoring - Different Objectives and Different Interpretation - 19109

Environmental surveillance and monitoring are a critical part of an overall strategy for protection of the public and environment. For site closure and disposal activities, monitoring and surveillance can be expected to continue many years into the future. In the case of environmental monitoring, two general types are commonly discussed: compliance and performance (or functional) monitoring. The objective of compliance monitoring is relatively clear. Compliance monitoring is conducted to demonstrate compliance with specific regulatory limits (e.g., maximum contaminant levels in groundwater). In this case, if your measurement is above these values, it is a regulatory concern. Monitoring can be especially challenging for engineered solutions involving robust barriers expected to last for decades or potentially hundreds or thousands of years. Although some gradual releases are expected to occur as barriers lose effectiveness, observable changes in groundwater are likely to be very small and may be delayed for very long times. For robust engineered systems, compliance in the near future is not expected to be a significant concern. The question becomes, what type of monitoring can be used to identify the potential for a compliance problem in the future? This has led to increasing use of performance monitoring as an early warning system. Rather than comparing against environmental standards, performance monitoring is compared against assumptions and results from a performance assessment. Performance monitoring is used to observe other potential indicators of changes in system behavior (vadose zone concentrations, changes in chemistry near the waste, changes in engineered features). Translating a change in a local measurement to a potential change in the concentration in an aquifer that would lead to a potential compliance concern in the future is a significant challenge for performance monitoring. Such monitoring needs to be more carefully interpreted and communicated than compliance monitoring. Considerations for implementation of performance monitoring are the focus of this paper. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Determining a location and size of a gas source with a spectrometer gas monitor

A process for determining a location and size of a gas source within an area with a spectrometer gas monitor includes: providing the spectrometer gas monitor; performing fence line monitoring of the area with the spectrometer gas monitor; collecting spectroscopic data over multiple open paths around the area with the spectrometer gas monitor; measuring atmospheric conditions along gas inflows and gas outflows of the area; subtracting a background for a selected gas from the spectroscopic data; applying a boundary constraint to the spectroscopic data; determining an atmospheric concentration of air entering the area; applying bootstrapping to the spectroscopic data; applying zero minimum elimination to the spectroscopic data; and producing inversion data from the spectroscopic data to determine the location and size of the gas source within the area.

03 NATURAL GAS↗

NASA NTRSDate not supplied

Monitoring Human Performance on Future Deep Space Missions Abstract

NASA and the commercial spacecraft community are working diligently to put the first woman on the moon in the 2024 timeframe. At the same time, NASA researchers are thinking about how to solve the even larger challenges that future deep space missions will bring. Space travel itself is difficult, but astronauts on deep space missions will face obstacles and unknowns never before experienced. In addition to the altered gravity and hostile/closed environment of a spacecraft, deep space crews will face increased radiation, isolation, and distance from Earth. During Extravehicular Activity (EVA, or “spacewalk”) operations, crew will experience increased physical and cognitive workload due to extended types, frequencies and durations of tasks performed on exploration missions in partial gravity environments. All of these stressors will impact crew physical and mental health and performance in difficult-to-anticipate ways. Crew autonomy may be one of the biggest challenges faced. Communication delays and blackouts will occur, and in those situations, the crew may not have access to the Mission Control Center (MCC). They may be forced to be solely dependent on each other and the available information onboard to stay alive, healthy, and achieve the mission. The only conceivable way to meet the challenges of Earth independence is to enable the crew to monitor their own health and performance -- preferably unobtrusively as they perform their duties. Technologies and techniques must be developed to aid the crew in these assessments. A deep space mission is expected to have relatively short periods of high cognitive demand, stress, and fatigue, alongside potentially long periods of cognitive underload during the transit, where boredom, loneliness, and depression can set in. Both ends of this spectrum are dangerous. Crew must be made aware when their task performance drops significantly, when their cognitive workload is too high, when they have lost situation awareness, or when they are too stressed or too fatigued to perform well. They must be able to identify these risks, and then mitigate them with countermeasures available onboard. A number of self-monitoring technologies are presently being explored by NASA to advance crew state determination capabilities. These range from real-time, physiological workload and situation awareness assessments, to crew health measurements determining physical and mental fitness for duty, to task performance metrics such as suit resource expenditures. For EVA tasks during surface exploration missions, biomedical information such as metabolic rate may be provided to crewmembers for situational awareness related to task performance efficiency. In addition, translation distances, hydration, nutrition, inspired CO2 exposure and other consumables usage rates may be useful input metrics for modeling individualized performance during tasks to inform crew or provide estimates of work efficiency. Oculomotor metrics such as gaze dwell time, pupilometry, and eye tracking collected in advanced helmet mounted displays could potentially be used to characterize crew situation awareness. This paper highlights some of these projects, and provides broader discussion about the need for advanced monitoring and smart technologies, as NASA takes the leap into the next generation of space exploration.

Kritina Holden↗

NASA NTRSDate not supplied

Monitoring Human Performance on Future Deep SpaceMissions

Kritina Anderson↗

DOE OSTI2021

Hybrid Analytics Solution to Improve Coal Power Plant Operations

This project focused on developing advanced methods for thermal performance monitoring of a coal-fueled power plant. The specific goal was to develop and demonstrate a new thermal performance monitoring approach using a hybrid model that integrates a physics-based heat balance model with a machine learning-based pattern recognition model. The hybrid model enables increased accuracy and scope of the thermal analysis and an improved ability to monitor and detect changes in plant operation. This new approach takes full advantage of the individual model capabilities and creates an important new set of capabilities not previously possible using the two types of models separately. Using the heat balance model, a rich set of derived parameters (virtual sensors) are calculated from the measured plant operating data at each time point. The combined measured and derived data values are used by machine learning algorithms to create pattern recognition models over the range of normal unit operation. To create the monitoring models, historical data from normal operation of the plant is first processed by the heat balance model to compute the derived parameter data. The result is a greatly expanded set of normal operating data that can be used as input to create the pattern recognition model. Once the models are calibrated for normal operation, the hybrid model is suitable for use in continuous online monitoring. During online monitoring, new plant operating data is processed first by the heat balance model and then by the pattern recognition model. Results from the pattern recognition model quantify the deviation of each measured or derived parameter from its expected value in normal operation. The hybrid models can detect abnormal changes in plant operating data with very high accuracy and sensitivity. When abnormal behavior is detected, alerts are generated automatically for evaluation by the plant monitoring staff. The new hybrid solution product was developed and verified in the performance of the project. The hybrid solution was tested first in a simulation environment that mimicked the plant data systems and infrastructure used by U.S. power generating plants and utilities. The hybrid solution was then deployed for real-time, online monitoring of an operating coal-fueled power plant at a field test site. Field testing demonstrated that all hybrid solution development objectives were accomplished. The project work was based on combining the capabilities of two existing software products to create the new hybrid solution product. One of these was the existing MapEx® heat balance product and the other was the existing SureSense® advanced pattern recognition product. Each of these separate products was assessed to be at a Technology Readiness Level (TRL) of 9 at the start of the effort. The hybrid solution product was assessed to be at a TRL of 2 at the start of the project based on early feasibility work by the project team. At completion of the field testing performed in the project, the hybrid solution product was assessed to be at a TRL of 7. The project team expects that the hybrid solution product will be deployed commercially and will achieve a TRL of 9 within one year after completion of the project.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Monitoring Plan for the Idaho National Laboratory Remote Handled Low Level Waste Disposal Facility

This monitoring plan for Idaho National Laboratory’s Remote-Handled Low Level Waste Disposal Facility was developed to meet the requirements for monitoring low-level waste disposal facilities according to the U.S. Department of Energy (DOE) Order 435.1, “Radioactive Waste Management,” and the guidance provided in the associated technical standard “Disposal Authorization Statement and Tank Closure Documentation” (DOE-STD-5002-2017). The purpose of this monitoring plan is to document a monitoring strategy that includes (1) compliance monitoring activities to demonstrate compliance with regulatory standards/limits and (2) performance monitoring to build confidence the facility is performing as demonstrated in the facility performance assessment (PA) (DOE-ID 2018a), composite analysis (CA) (DOE ID 2012), and CA addendum (DOE-ID 2018b). The de minimus impact to the aquifer predicted by the PA suggests that aquifer compliance monitoring should be augmented with performance monitoring of the drainage course materials and sedimentary interbeds in the vadose zone beneath the facility to provide a more effective means of identifying performance deviations. The monitoring approach delineated in this document was informed by the systems evaluation of natural and engineered facility features presented in the PA, an assessment of aquifer baseline conditions (INL 2017d), the dose analysis conducted in support of the PA and CA, and monitoring data collected during the first four years of facility operations (baseline monitoring phase) (INL 2023b). This plan provides monitoring locations, sampling frequencies, and sampling methods; recommendations for data evaluation; and a description of the monitoring plan implementation. Collected data will be used to demonstrate facility compliance and to identify conditions that are not consistent with the key assumptions made by the PA and CA.

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗

NASA NTRS1975

Display/control requirements for VTOL aircraft

Quantative metrics were determined for system control performance, workload for control, monitoring performance, and workload for monitoring. Pilot tasks were allocated for navigation and guidance of automated commercial V/STOL aircraft in all weather conditions using an optimal control model of the human operator to determine display elements and design.

Hoffman, W. C.↗

NASA NTRS1996

Distributed Large Data-Object Environments: End-to-End Performance Analysis of High Speed Distributed Storage Systems in Wide Area ATM Networks

We have developed and deployed a distributed-parallel storage system (DPSS) in several high speed asynchronous transfer mode (ATM) wide area networks (WAN) testbeds to support several different types of data-intensive applications. Architecturally, the DPSS is a network striped disk array, but is fairly unique in that its implementation allows applications complete freedom to determine optimal data layout, replication and/or coding redundancy strategy, security policy, and dynamic reconfiguration. In conjunction with the DPSS, we have developed a 'top-to-bottom, end-to-end' performance monitoring and analysis methodology that has allowed us to characterize all aspects of the DPSS operating in high speed ATM networks. In particular, we have run a variety of performance monitoring experiments involving the DPSS in the MAGIC testbed, which is a large scale, high speed, ATM network and we describe our experience using the monitoring methodology to identify and correct problems that limit the performance of high speed distributed applications. Finally, the DPSS is part of an overall architecture for using high speed, WAN's for enabling the routine, location independent use of large data-objects. Since this is part of the motivation for a distributed storage system, we describe this architecture.

Johnston, William↗

NASA NTRS2018

Know Your Enemy - Implementation of Bioremediation within a Suspected DNAPL Source Zone Following High-Resolution Site Characterization at Contractors Road Heavy Equipment Area, Kennedy Space Center, Florida

The National Aeronautics and Space Administration (NASA), Kennedy Space Center (KSC), has adopted high-resolution site characterization (HRSC) sampling techniques during baseline sampling prior to implementation of remedies to confirm and refine the conceptual site model (CSM). HRSC sampling was performed at Contractors Road Heavy Equipment Area (CRHE) prior to bioremediation implementation to verify the extent of the trichloroethene (TCE) dense non-aqueous phase liquid (DNAPL) source area (defined as the area with TCE concentrations above 1% solubility) and its daughter product dissolved plume that had been identified during previous HRSC events. The results of HRSC pre-bioremediation implementation sampling suggested that the TCE source area was larger than originally identified during initial site characterization activities, leading to a design refinement to improve electron donor distribution and increase the likelihood of achieving remedial objectives. Approach/Activities: HRSC was conducted from 2009 through 2014 to delineate the vertical and horizontal extent of chlorinated volatile organic compounds (CVOCs) in the groundwater. Approximately 2,340 samples were collected from 363 locations using direct push technology (DPT) groundwater sampling techniques. Samples were collected from up to 14 depth intervals at each location using a 4-foot sampling screen. This HRSC approach identified a narrow (approx. 5 to 30 feet wide), approximately 3,000 square foot TCE DNAPL source area (maximum detected TCE concentration of 160,000 micrograms per liter [micro-g/L] at DPT sampling location DPT0225). Prior to implementation of a bioremediation interim measure, HRSC baseline sampling was conducted using DPT groundwater sampling techniques. Concentrations of TCE were an order of magnitude lower than previous reported (12,000 micro-g/L maximum at DPT sampling location DPT0225) at locations sampled adjacent to previous sampling locations. To further evaluate the variability in concentrations observed additional sampling was conducted in 2016. The results identified higher concentrations than originally detected within the previously defined source area and the presence of source zone concentrations upgradient of the previously defined source area (maximum concentration observed 570,000 micro-g/L). The HRSC baseline sampling data allowed for a revision of the bioremediation design prior to implementation. Bioremediation was implemented within the eastern portion of the source area in November and December 2016 and quarterly performance monitoring was completed in March and June 2017. Reductions in CVOC concentrations from baseline were observed at all performance monitoring wells in the treatment area, and by June 2017, an approximate 95% CVOC mass reduction was observed based on monitoring well sampling results. Results/Lessons Learned: The results of this project suggest that, due to the complexity of DNAPL source zones, HRSC during pre-implementation baseline sampling in the TCE source zone was an essential strategy for verifying the treatment area and depth prior to remedy implementation. If the upgradient source zone mass was not identified prior to bioremediation implementation, the mass would have served as a long-term source for the dissolved plume.

Chrest, Anne↗

DOE OSTI2019

Evaluating Environmental Remediation Performance at Radwaste Sites Using Multiple, Censored Regression Analysis - 19348

Located on the U.S. Department of Energy's Hanford Site in Richland,Washington, the 300 Area's remediation performance monitoring requires assessment of concentration trends and calculation of means and lower and upper confidence limits on the mean. The performance monitoring wells for uranium are located within 400 m of the Columbia River and groundwater elevations and uranium concentrations are influenced by groundwater-surface water interactions. A multiple regression approach was used to assess trends because multiple covariates can be used to account for the observed fluctuations in groundwater elevation and concentration. Incorporation of covariates accounting for these groundwater-surface water interactions (i.e., river stage) reduces the random error in the trend estimation and improves the estimates of remediation performance metrics (i.e., mean and confidence limits on the mean) over traditional, non-parametric methods. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1992

Aircraft Engine-Monitoring System And Display

Proposed Engine Health Monitoring System and Display (EHMSD) provides enhanced means for pilot to control and monitor performances of engines. Processes raw sensor data into information meaningful to pilot. Provides graphical information about performance capabilities, current performance, and operational conditions in components or subsystems of engines. Provides means to control engine thrust directly and innovative means to monitor performance of engine system rapidly and reliably. Features reduce pilot workload and increase operational safety.

Abbott, Terence S.↗

NASA NTRS2016

Supply Warehouse#3, SWMU 088 Operations, Maintenance, and Monitoring Report Kennedy Space Center, Florida

This document presents the findings, observations, and results associated with Operations, Maintenance, and Monitoring (OM&M) activities of Corrective Measures Implementation (CMI) activities conducted at Supply Warehouse #3 (SW3) located at John F. Kennedy Space Center (KSC), Florida from October 8, 2015, to September 12, 2016, and performance monitoring results for semi-annual sampling events conducted in March and September 2016. The primary objective of SW3 CMI is to actively decrease concentrations of trichloroethene (TCE) and vinyl chloride (VC) to less than Florida Department of Environmental Protection (FDEP) Natural Attenuation Default Concentrations (NADCs), and the secondary objective is to reduce TCE, cis-1,2-dichloroethene (cDCE), trans-1,2-dichloroethene (tDCE), 1,1-dichloroethene (11DCE), and VC concentrations to less than FDEP Groundwater Cleanup Target Levels (GCTLs). The SW3 facility has been designated Solid Waste Management Unit (SWMU) 088 under KSC's Resource Conservation and Recovery Act (RCRA) Corrective Action Program. Based on the results to date, the SW3 air sparging (AS) system is operating at or below the performance criteria as presented in the 2008 SW3 Corrective Measures Implementation (CMI) Work Plan and 2009 and 2012 CMI Work Plan Addenda. Since the start of AS system operations on December 19, 2012, through the September 2016 groundwater sampling event, TCE concentrations have decreased to less than the GCTL in all wells within the Active Remediation Zone (ARZ), and VC results remain less than NADC but greater than GCTL. Based on these results, team consensus was reached at the October 2016 KSC Remediation Team (KSCRT) meeting to continue AS system operations and semi-annual performance monitoring of volatile organic compounds in March 2017 at ten monitoring wells at select locations, and in September 2017 at four monitoring wells at select locations to reduce VC concentrations to below GCTL. Additionally, surface water samples will be collected at locations SW0001, SW0002, and SW0003 during both the March and September 2017 events. Team consensus was also reached at the October 2017 KSCRT meeting to continue with operation and maintenance (O&M) of the AS system at SW3.

REPORT↗

NASA NTRS2007

System and Method for Monitoring Piezoelectric Material Performance

A system and method are provided for monitoring performance capacity of a piezoelectric material that may form part of an actuator or sensor device. A switch is used to selectively electrically couple an inductor to the piezoelectric material to form an inductor-capacitor circuit. Resonance is induced in the inductor-capacitor circuit when the switch is operated to create the circuit. The resonance of the inductor-capacitor circuit is monitored with the frequency of the resonance being indicative of performance capacity of the device's piezoelectric material.

Moses, Robert W.↗

NASA NTRS1966

Human performance control monitoring systems Interim report no. 2

Computer program to simulate second order servo system dynamics under automatic and manual control

MANUAL CONTROL↗

DOE OSTI2022

Characterizing particle-based thermal storage performance using optical methods for use in next generation concentrating solar power plants

Concentrating Solar Power (CSP) generation is an attractive option for low-emission power generation; however, the high costs of thermal storage associated with concentrating solar create a large barrier for their use and adaptation into modern life. Lowering their operation costs, while maintaining high thermal storage and transfer performance is essential. Solid particle-based heat exchange systems can reduce CSP cost but are often less efficient. Efforts to increase their performance have led to use of binary size particle mixes. Presented is an optical-based thermal analysis technique used to measure near-wall thermal conductivity of particle beds essential in determining their heat exchanger efficiency. Modulated Photothermal Radiometry is used to make dynamic temperature measurements, allowing for the extraction of the most relevant thermal properties like thermal conductivity, specific heat, and effusivity. The system uses a modulated laser source causing a damped periodic heat flux, resulting in a frequency and thermal property dependent surface temperature, of which is measured using radiometry. Lock-In techniques are used to extrapolate the amplitude of the signal. Plotting the amplitude against the root angular frequency allows for effusivity measurement by ratio to a known sample. Using specific heat measurements from literature and density measurements, the thermal conductivity of the particle mixes can be calculated. The simplicity of MPTR to probe through the depth of the bed is ideal for use in CSP for dynamic thermal performance monitoring.

Corona, Javier↗

NASA NTRS2020

Area South of K7-516, SWMU 100 Operations, Maintenance, and Monitoring Report Kennedy Space Center, Florida

This Operations, Maintenance, and Monitoring Report documents groundwater interim measure activities from March 30, 2019 through December 31, 2019 at the Area South of K7-516 site located at Kennedy Space Center (KSC), Florida. An air sparge (AS) system consisting of 16 AS wells was installed along the Barge Canal in 2012 to be protective of the Outstanding Florida Waters (OFW). The AS system was expanded in 2015 by installing 40 AS wells to remediate the hot spot (HS) area. Additional expansion activities were performed in 2018 through the installation of eight additional AS wells along the Barge Canal. Currently 24 OFW and 40 HS AS wells are in operation. This report includes performance monitoring events for the OFW and HS areas, along with an annual Interim Groundwater Monitoring (IGWM) event in the Western Area. The 516S site has been designated Solid Waste Management Unit 100 under KSC’s Resource Conservation and Recovery Act Corrective Action program. This document was prepared by Tetra Tech, Inc., for the National Aeronautics and Space Administration under Indefinite Delivery Indefinite Quantity Contract 80KSC019D0011/80KSC019F0068. The OFW and HS AS systems are meeting the performance criteria and IM objectives of preventing contaminant migration and remediating groundwater within the treatment zone. Team consensus was reached to continue operation of the combined OFW and HS AS systems (Meeting Minute 2002-M02, Decisions 2002-D06, D09 and D04). Team consensus was also reached to continue semi-annual OFW performance monitoring and annual monitoring for the IGWM Western Area for the same wells sampled during 2019 events. In the eastern area, MW13, MW14, and MW15 will be reduced from semi-annual to annual frequency. For the HS Area, team consensus was reached to continue the current annual/biennial monitoring program (Meeting Minute 2002-M02, Decisions 2002-D02, D03, D05, D07 and D08).

Patrice L. Lehocky↗

NASA NTRS1998

Guidance and Control Concepts for the X-33 Technology Demonstrator

The X-33 technology demonstrator is a suborbital precursor to the Reusable Launch Vehicle (RLV) with first flight planned for summer of 1999. The flight test program will include about 15 flights originating from Edwards Air Force Base, California, each with widely varying flight profiles in order to test new thermal protection system (TPS) materials, structures, and linear aerospike engines. The first flights will be relatively short range flights with about a 300 nmi range, maximum Mach number of 7, maximum altitude of 190,000 feet, whereas the latter flights will cover about 800 nmi range, with max altitude of about 260,000 feet and max Mach of about 15. The guidance algorithms must be flexible enough to accommodate these various profiles and to adapt to severe off-nominal dispersions, such as early engine failure (partial or total) where possibly more than half the thrust is lost. An onboard real-time performance monitor will be used to assess the viability of the nominal landing site as well as alternate landing sites that would potentially be used in extreme off-nominal conditions. During ascent, a single entry guidance-related parameter, which is easy to calculate, is used to assess the viability of the nominal landing site as well as alternate landing sites. Real-time adjustment of the stored ascent attitude profile will be performed, as required, to maximize the probability of making it to the nominal landing site. Numerical results are given for various engine-out cases to illustrate the adaptability of the performance monitor.

Dukeman, Gregory A.↗

NASA NTRSDate not supplied

Evaluation of Markerless Motion Capture for Monitoring Sensorimotor Performance

BACKGROUND Astronauts returning from long-duration exposure to microgravity frequently exhibit alterations in sensorimotor function leading to postural imbalance, impaired locomotion, and operational challenges to manual control. Mission duration and individual responses often influence both the severity of performance decrements and the variability in adaptation timelines. Postflight disruptions during functional tasks are often detected through body-worn inertial measurement unit (IMU) devices. While IMU sensors are relatively compact, the long-term wear may lead to discomfort, displacement of the sensors on the body, and restrictions in movement or crew behavior. Although IMU data offers valuable insights from a research standpoint, interpreting changes in pre- and post-flight measures can be difficult for crew support personnel beyond the research domain, which can hinder the application for medical assessments and rehabilitation. Finally, the availability of inertial sensors in-flight is limited. There is a need for unobtrusive monitoring tools to improve our ability to monitor adaptation following gravitational transitions in various postflight evaluations and rehabilitation settings. Markerless motion capture (MMC) is an evolving unobtrusive technology that builds upon decades of research with marker-based motion capture systems to provide 3D human pose estimation from multiple synchronized 2D camera views using deep learning algorithms. Markerless technology can revolutionize how data is captured pre- and post-flight and potentially in-flight during intravehicular activity by enabling pose estimation of multiple crew members from onboard camera hardware. METHODS The following presents the initial evaluation of a state-of-the-art commercial-off-the-shelf MMC system, Theia Markerless, compared to IMU devices during various ground-based functional tasks and environmental conditions. The featured functional tasks include assessments from Human Research Program (HRP) funded studies such as Sensorimotor Standard Measures and Sensorimotor Assessments. Synchronous data collected using both motion capture and IMUs are analyzed for six male and female subjects of varying anthropometry. The analysis includes limited assessments of clothing, capture volume configurations, and the tool's sensitivity to detecting performance changes after a spaceflight analog centrifuge exposure. The development of visualization tools to enhance the application of the pose estimation output is also presented. RESULTS Initial results demonstrate comparable root mean square error (RMSE) to existing literature evaluating markerless and marker-based motion capture systems. Considering the relative functional range of motion of the cervical spine, normalized error values for the markerless system’s accuracy of the head was 0.032 in pitch, 0.025 in roll, and 0.018 in yaw plane of motion across a subset of functional tasks. The raw RMSE values were 3.49, 2.25, and 2.81 degrees respectively. For the torso, results suggest normalized errors of 0.469 in pitch, 0.191 in roll, and 0.307 in yaw planes of motion and raw RMSE values of 3.52, 1.53, and 3.07 degrees respectively. The data suggests the functional demands of a particular task influences the estimation accuracy of the MMC system where more dynamic motion and cases where subjects are not upright may introduce diminished tracking accuracy. DISCUSSION The following work lays the foundation for future implementations leveraging markerless motion capture to assess the time course of recovery and provide insight for rehabilitation protocols to enhance crew readiness for the resumption of daily activities. These tools offer effective methods for anonymizing sensitive crew data, facilitating numerous applications across research, medical, and rehabilitation groups. Collaborations with the Anthropometry and Biomechanics Facility will provide further comparisons of the Markerless system to a marker-based system. ACKNOWLEDGEMENT</ This work is supported by NASA’s Exploration Systems Development Mission Directorate Mars Campaign Office Crew Health Countermeasures.

Hannah M. Weiss↗