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Using human performance and physiological measures during lunar surface mission simulation to evaluate minimum volume cabin design for lunar roving vehicle
The purpose of this study was to compare flow constrained area (FCA) capacity setting methods for Collaborative Trajectory Options Program (CTOP) as they pertain to the Integrated Demand Management (IDM) concept. IDM uses flow balancing to manage air traffic across multiple FCAs with a common downstream constraint, as well as constraints at the respective FCA locations. FCA capacity rates can be set manually, but generating capacities for multiple, interdependent FCAs could potentially over-burden a user. A new enhancement to CTOP called the FCA Balance Algorithm (FBA) was developed at NASA Ames Research Center to improve the process of allocating capacity across multiple flow constrained segments in the airspace. The FBA evaluates the predicted demand and capacity across multiple FCAs and dynamically generates capacity settings for the FCAs that best meet capacity limits for all identified constraints. In a human-in-the-loop simulation study, both manual and automated capacity setting methods were evaluated in terms of their overall feasibility using measures of system performance, human performance, and qualitative feedback. Subject matter experts were asked to use three different methods to allocate capacity to three FCAs, either (1) by manually setting capacity for every 60-minute time window, (2) by manually setting capacity for every 15-minute time window, or (3) by using the FBA capability to automatically generate capacity settings. Results showed no significant differences in terms of overall system performance, indicated by similar ground delay and airport throughput numbers between methods. However, differences in individual strategies afforded by the manual methods allowed some participants to achieve system-wide delay that was much lower than the average. The FBA was the fastest method of capacity setting, and it received the lowest subjective rating scores on physical task load, mental task load, task difficulty and task complexity out of the three methods. Finally, participants explained through qualitative feedback that there were many benefits to using the FBA, such as ease of use, accuracy, and low risk of human input error. Participants did not experience the same limitations with the FBA that they did with the manual methods, such as reduced accuracy in the 60-minute manual condition, or high complexity in the 15-minute/manual condition. These results suggest that the FBA automation enhancement to CTOP maintains system performance while improving human performance. Therefore, the FBA could be introduced as a way to mitigate operator workload while planning a CTOP.
The purpose of this study was to compare flow constrained area (FCA) capacity setting methods for Collaborative Trajectory Options Program (CTOP) as they pertain to the Integrated Demand Management (IDM) concept. IDM uses flow balancing to manage air traffic across multiple FCAs with a common downstream constraint, as well as constraints at the respective FCA locations. FCA capacity rates can be set manually, but generating capacities for multiple, interdependent FCAs could potentially over-burden a user. A new enhancement to CTOP called the FCA Balance Algorithm (FBA) was developed at NASA Ames Research Center to improve the process of allocating capacity across multiple flow constrained segments in the airspace. The FBA evaluates the predicted demand and capacity across multiple FCAs and dynamically generates capacity settings for the FCAs that best meet capacity limits for all identified constraints. In a human-in-the-loop simulation study, both manual and automated capacity setting methods were evaluated in terms of their overall feasibility using measures of system performance, human performance, and qualitative feedback. Subject matter experts were asked to use three different methods to allocate capacity to three FCAs, either (1) by manually setting capacity for every 60-minute time window, (2) by manually setting capacity for every 15-minute time window, or (3) by using the FBA capability to automatically generate capacity settings. Results showed no significant differences in terms of overall system performance, indicated by similar ground delay and airport throughput numbers between methods. However, differences in individual strategies afforded by the manual methods allowed some participants to achieve system-wide delay that was much lower than the average. The FBA was the fastest method of capacity setting, and it received the lowest subjective rating scores on physical task load, mental task load, task difficulty and task complexity out of the three methods. Finally, participants explained through qualitative feedback that there were many benefits to using the FBA, such as ease of use, accuracy, and low risk of human input error. Participants did not experience the same limitations with the FBA that they did with the manual methods, such as reduced accuracy in the 60-minute manual condition, or high complexity in the 15-minute/manual condition. These results suggest that the FBA automation enhancement to CTOP maintains system performance while improving human performance. Therefore, the FBA could be introduced as a way to mitigate operator workload while planning a CTOP.
Ionizing radiation presents a major challenge to human exploration and long-term residence in space. The deep-space radiation spectrum includes highly energetic particles that generate double strand breaks (DSBs), deleterious DNA lesions that are usually repaired without errors via homologous recombination (HR), a conserved pathway in all eukaryotes. While progress identifying and characterizing biological radiation effects using Earth-based facilities has been significant, no terrestrial source duplicates the unique space radiation environment.We are developing a biosensor-based nanosatellite to fly aboard NASAs Space Launch System Exploration Mission 1, expected to launch in 2017 and reach a 1AU (astronomic unit) heliocentric orbit. Our biosensor (called BioSentinel) uses the yeast S. cerevisiae to measure DSBs in response to ambient space radiation. The BioSentinel strain contains engineered genetic defects that prevent growth until and unless a radiation-induced DSB near a reporter gene activates the yeasts HR repair mechanisms. Thus, culture growth and metabolic activity directly indicate a successful DSB-and-repair event. In parallel, HR-defective and wild type strains will provide survival data. Desiccated cells will be carried within independent culture microwells, built into 96-well microfluidic cards. Each microwell set will be activated by media addition at different time points over 18 months, and cell growth will be tracked continuously via optical density. One reserve set will be activated only in the occurrence of a solar particle event. Biological measurements will be compared to data provided by onboard physical dosimeters and to Earth-based experiments.BioSentinel will conduct the first study of biological response to space radiation outside Low Earth Orbit in over 40 years. BioSentinel will thus address strategic knowledge gaps related to the biological effects of space radiation and will provide an adaptable platform to perform human-relevant measurements in multiple space environments. We hope that it can therefore be used on the ISS, on and around other planetary bodies as well as other exploration platforms as a self-contained system that will allow us to compare and calibrate different radiation environments.BioSentinels results will be critical for improving interpretation of the effects of space radiation exposure, and for reducing the risk associated with long-term human exploration.
Background: Ensuring the validity of results from funded programs is a critical concern for agencies that sponsor biological research. In recent years, the open science movement has sought to promote reproducibility by encouraging sharing not only of finished manuscripts but also of data and code supporting their findings. While these innovations have lent support to third-party efforts to replicate calculations underlying key results in the scientific literature, fields of inquiry where privacy considerations or other sensitivities preclude the broad distribution of raw data or analysis may require a more targeted approach to promote the quality of research output. Methods: We describe efforts oriented toward this goal that were implemented in one human performance research program, Measuring Biological Aptitude, organized by the Defense Advanced Research Project Agency's Biological Technologies Office. Our team implemented a four-pronged independent verification and validation (IV&V) strategy including 1) a centralized data storage and exchange platform, 2) quality assurance and quality control (QA/QC) of data collection, 3) test and evaluation of performer models, and 4) an archival software and data repository. Results: Our IV&V plan was carried out with assistance from both the funding agency and participating teams of researchers. QA/QC of data acquisition aided in process improvement and the flagging of experimental errors. Holdout validation set tests provided an independent gauge of model performance. Conclusions: In circumstances that do not support a fully open approach to scientific criticism, standing up independent teams to cross-check and validate the results generated by primary investigators can be an important tool to promote reproducibility of results.
BACKGROUND The Human Factors and Behavioral Performance Exploration Measures (HFBP-EM) suite is a set of standardized measures used to assess behavioral health and performance risks related to future exploration-class space missions. During the HFBP-EM suite development, the importance of assessing unique contextual factors to understand the drivers of team dynamics was identified. Group living skills are a unique workplace skill which applies to those coworkers who live and work together. As mission lengths become longer, the line between work and non-work life may blur. Extreme living conditions such as spaceflight can result in increased stress over time, negatively impact mood and well-being, decrease team cohesion and increase conflict, deteriorate task performance, and potentially reduce mission success [1]. With no breaks in their isolated and confined environment possible, living with a messy, inconsiderate crewmember can add to the stress. Alternatively, crews with good group living skills may support one another, mitigating the negative effects of the extreme environment. There was no direct measure of group living skills. This work sought to fill this measurement gap and collect data related to group living in isolated, confined, extreme (ICE) environments. METHODS Group living skills as an astronaut competency area were developed with the input of experts familiar with ICE environments. Distinct aspects of this competency were reviewed and discussed by a NASA-invited group of spaceflight experts in 2015 to identify individual and team behavioral health and performance measures for operational environments. This work resulted in a 5-item measure focused on group living experiences with each crewmember (or the crew as a whole). A 6th item asks whether an individual would go on another mission with a particular crewmember or crew as a measure of future-oriented group living, or team viability. The measure was deployed in 24 teams living and working together in isolated and confined environments (e.g., International Space Station, space mission simulation analogs). RESULTS & DISCUSSION In this presentation, we will summarize results which indicate the Group Living Skills (GLS) Survey is a reliable, valid, and operationally feasible measure. Reliability was evaluated in several ways. Two-way multilevel mixed models revealed strong repeated measures reliability (ICC2 = .902, Omega between = .954). Reliability of change within teams was strong (Rc = .81). Factor analysis showed a consistent two-factor structure when individuals rated either the whole crew, all individuals, just peers, or a focal roommate. The latter used a sociometric approach to rating the GLS of the roommates. The two factors were tidiness and being considerate of others. We assessed the criterion-related validity of GLS, by examining GLS operationalized as the team-level aggregated GLS Survey scores as a predictor of team viability, team cohesion, and team performance. We used generalized mixed models to account for the repeated measures and longitudinal data. The marginal R2 change was used to determine the extent to which GLS were related to the team outcomes while controlling for mission day and campaign. Results suggested GLS was strongly related to team viability (marginal R2 change = .40), team cohesion (marginal R2 change = 0.28), social cohesion (marginal R2 change = 0.25), and moderately related to task cohesion (marginal R2 change = 0.18). GLS scores had a small relationship with team performance (marginal R2 change = .08). Results support GLS as a measure that can be used to capture group living skills of crews who live and work together in ICE. Because the items were written to be broadly applicable, less extreme environments with teams living and working together for some length of time also benefit from this measure (e.g., college roommates, camps). See Landon et al. (2024) for full results [2]. SUMMARY We report reliability and validity evidence for a new measure that assesses the Group Living Skills for teams that live and work in operational environments such as spaceflight.
This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.
This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.
Subjective measures of physical exertion, compensation, and controllability while performing tasks in simulated reduced gravity can be affected by changing the center of gravity (CG) of the overall system. The CG of the overall system is defined as the combined CG of the subject, the spacesuit, and the equipment required to change the CG. PURPOSE: To determine if changing the CG affects subjective ratings of suited human performance in simulated lunar gravity. METHODS: A custom weight support structure interfaced with the lunar prototype spacesuit, allowing manipulation of the CG. Weight locations to alter CG were based on a reference subject (81.6 kg, 182.9 cm). Six subjects (80.0 +/- 10.6 kg, 182.3 +/- 6.2 cm) completed 4 tasks (walking, kneel/stand, rock pickup, and shoveling) with system CG at 3 different locations (B=4.8/1.0, C=7.6/14.4, and P=11.2/20.1 cm aft/above the standard subject?s CG). Lunar gravity (0.17-g) was simulated by parabolic flight. Suited testing was performed at 29.6 kPa with a combined suit and structure mass of 181 kg. In all conditions, subjects provided ratings of perceived exertion (RPE) and the gravity compensation and performance scale (GCPS) upon completion of each task. RESULTS: Mean RPE and GCPS were highest at P for all tasks. Variability was greatest at B and lowest at C, and large variations between subjects at the same CG existed for both RPE and GCPS. These trends were not consistent with results from unsuited CG studies performed in other underwater and overhead suspension lunar gravity simulations. CONCLUSION: Modifying CG during suited testing at lunar gravity seems to affect subjective human performance. However, variation in subjective ratings at a given CG location indicates that further study is needed to determine the interactions among lunar gravity simulation, system CG, system mass, and subject characteristics such as anthropometry, strength, and fitness.
A new performance measure is introduced for multivariable closed loop experiments with a human operator. The essential feature of the phase margin performance measure (PMPM) is that the performance of each control loop can be determined independently, with prescribed disturbance and error levels. A variable filter parameter is used as the PMPM within the loop and it assures a high workload at the same time. There is a straightforward relationship between the PMPM and the inner loop feedback augmentation that can be utilized in trade-off studies. An adjustment scheme that seeks the PMPM automatically is described as employed in a single loop control task. This task applies directly to the experimental study of displays for helicopters and VTOL aircraft.
Modernization of U.S. nuclear power plants (NPPs) is widespread, with most plants currently replacing and transitioning equipment, control systems, and human system interfaces (HSI)s from analog to digital displays. This conversion remedies the obsolescence of analog parts along with needs for increased intuitiveness of design, safety, and capabilities. The Human Factors and Reliability team at Idaho National Laboratory (INL) carried out twelve control room modernization studies in the newly designed Human Systems Simulation Laboratory (HSSL) over nine years. The HSSL was constructed as a testbed for evaluating human factors techniques and performance measures, HSI frameworks, and cutting-edge operational concepts in NPPs. Installing a full-scope training simulator enabled direct design and evaluation work on the same instrumentation and control (I&C) and HSIs located at U.S. plants. The subsequent addition of glass top bays afforded crews opportunities to implement operations via the simulator using full-scale representations of their home NPP. Additionally, functional HSI prototypes were created, providing an environment for operator-in-the-loop benchmark studies. The HSSL has assisted in upgrades of six commercial NPP control rooms and served as an invaluable proving ground for new NPP operations technology. Human reliability analysis (HRA) was not originally the focus of the studies; however, data relating to HRA such as type and frequency of human errors can be extracted from the studies. INL is currently extracting data from the HSSL study reports to apprise how information gathered from simulation, HSI, and other related studies can create a broad look across different data sources to help inform HRA methods.
Many methods of measuring and predicting operator workload have been developed that provide useful information in the design, evaluation, and operation of complex systems and which aid in developing models of human attention and performance. However, the relationships between such measures, imposed task demands, and measures of performance remain complex and even contradictory. It appears that we have ignored an important factor: people do not passively translate task demands into performance. Rather, they actively manage their time, resources, and effort to achieve an acceptable level of performance while maintaining a comfortable level of workload. While such adaptive, creative, and strategic behaviors are the primary reason that human operators remain an essential component of all advanced man-machine systems, they also result in individual differences in the way people respond to the same task demands and inconsistent relationships among measures. Finally, we are able to measure workload and performance, but interpreting such measures remains difficult; it is still not clear how much workload is too much or too little nor the consequences of suboptimal workload on system performance and the mental, physical, and emotional well-being of the human operators. The rationale and philosophy of a program of research developed to address these issues will be reviewed and contrasted to traditional methods of defining, measuring, and predicting human operator workload. Viewgraphs are given.
The crew activity analyzer (CAA) is a system of electronic hardware and software for automatically identifying patterns of group activity among crew members working together in an office, cockpit, workshop, laboratory, or other enclosed space. The CAA synchronously records multiple streams of data from digital video cameras, wireless microphones, and position sensors, then plays back and processes the data to identify activity patterns specified by human analysts. The processing greatly reduces the amount of time that the analysts must spend in examining large amounts of data, enabling the analysts to concentrate on subsets of data that represent activities of interest. The CAA has potential for use in a variety of governmental and commercial applications, including planning for crews for future long space flights, designing facilities wherein humans must work in proximity for long times, improving crew training and measuring crew performance in military settings, human-factors and safety assessment, development of team procedures, and behavioral and ethnographic research. The data-acquisition hardware of the CAA (see figure) includes two video cameras: an overhead one aimed upward at a paraboloidal mirror on the ceiling and one mounted on a wall aimed in a downward slant toward the crew area. As many as four wireless microphones can be worn by crew members. The audio signals received from the microphones are digitized, then compressed in preparation for storage. Approximate locations of as many as four crew members are measured by use of a Cricket indoor location system. [The Cricket indoor location system includes ultrasonic/radio beacon and listener units. A Cricket beacon (in this case, worn by a crew member) simultaneously transmits a pulse of ultrasound and a radio signal that contains identifying information. Each Cricket listener unit measures the difference between the times of reception of the ultrasound and radio signals from an identified beacon. Assuming essentially instantaneous propagation of the radio signal, the distance between that beacon and the listener unit is estimated from this time difference and the speed of sound in air.] In this system, six Cricket listener units are mounted in various positions on the ceiling, and as many as four Cricket beacons are attached to crew members. The three-dimensional position of each Cricket beacon can be estimated from the time-difference readings of that beacon from at least three Cricket listener units
Signal Detection Theory (SDT) can be used to assess decision making performance in tasks that are not commonly thought of as perceptual. SDT takes into account both the sensitivity and biases in responding when explaining the detection of external events. In the standard SDT tasks, stimuli are selected in order to reveal the sensory capabilities of the observer. SDT can also be used to describe performance when decisions must be made as to the classification of easily and reliably sensed stimuli. Numbers are stimuli that are minimally affected by sensory processing and can belong to meaningful categories that overlap. Multiple studies have shown that the task of categorizing numbers from overlapping normal distributions produces performance predictable by SDT. These findings are particularly interesting in view of the similarity between the task of the categorizing numbers and that of determining the status of a mechanical system based on numerical values that represent sensor readings. Examples of the use of SDT to evaluate performance in decision tasks are reviewed. The methods and assumptions of SDT are shown to be effective in the measurement, evaluation, and prediction of human performance in such tasks.
The purpose of this section of the plan is to establish a development and implementation strategy plan for improving safety and efficiency in the Air Traffic Control (ATC) system. These improvements will be achieved through the proper applications of human factors considerations to the present and future systems. The program will have four basic goals: (1) prepare for the future system through proper hiring and training; (2) develop a controller work station team concept (managing human errors); (3) understand and address the human factors implications of negative system results; and (4) define the proper division of responsibilities and interactions between the human and the machine in ATC systems. This plan addresses six program elements which together address the overall purpose. The six program elements are: (1) determine principles of human-centered automation that will enhance aviation safety and the efficiency of the air traffic controller; (2) provide new and/or enhanced methods and techniques to measure, assess, and improve human performance in the ATC environment; (3) determine system needs and methods for information transfer between and within controller teams and between controller teams and the cockpit; (4) determine how new controller work station technology can optimally be applied and integrated to enhance safety and efficiency; (5) assess training needs and develop improved techniques and strategies for selection, training, and evaluation of controllers; and (6) develop standards, methods, and procedures for the certification and validation of human engineering in the design, testing, and implementation of any hardware or software system element which affects information flow to or from the human.
In the study of the dynamics and kinematics of the human body a wide variety of technologies has been developed. Photogrammetric techniques are well documented and are known to provide reliable positional data from recorded images. Often these techniques are used in conjunction with cinematography and videography for analysis of planar motion, and to a lesser degree three-dimensional motion. Cinematography has been the most widely used medium for movement analysis. Excessive operating costs and the lag time required for film development, coupled with recent advances in video technology, have allowed video based motion analysis systems to emerge as a cost effective method of collecting and analyzing human movement. The Anthropometric and Biomechanics Lab at Johnson Space Center utilizes the video based Ariel Performance Analysis System (APAS) to develop data on shirtsleeved and space-suited human performance in order to plan efficient on-orbit intravehicular and extravehicular activities. APAS is a fully integrated system of hardware and software for biomechanics and the analysis of human performance and generalized motion measurement. Major components of the complete system include the video system, the AT compatible computer, and the proprietary software.
Human performance characteristics in manual control tasks, and techniques for data analysis and systems simulation
Discussed here is the use of ultrasonic techniques originally used to locate cracks in metal structues to measure burn wound depth in humans. Acoustic impedance, performance tests, and the theoretical model are discussed. Measurements of skin burns on anesthetized pigs made with the the ultrasonic instrumentation were in agreement with diagnoses made by a physician, and subsequently confirmed by the healing process. Researchers felt that the concept proved useful in a clinical setting and that the instrument and concept were ready to extend to the manufacturer.