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Sudhakar Rajulu

Publications and source records attributed to Sudhakar Rajulu.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Effect of Varying HUT Sizing on Human-Spacesuit Contact Patterns

Fit, comfort, and mobility of the spacesuit rely on the location, magnitude, and type of contact between the suit and wearer. Typically, spacesuits include the Hard Upper Torso (HUT) comprising the upper torso of the spacesuit. Potential injuries from hard contact between the wearer’s shoulders and the HUT are a concern; therefore, to investigate spacesuit-wearer contact patterns, data from a retrospective study was analyzed. This study had eight subjects wearing the Shuttle Extravehicular Mobility Unit (EMU) spacesuit in two configurations, one with a nominally sized HUT and another with an oversized HUT. The subjects were sized per the NASA standard suit fit procedure. The suit’s arms and lower body were also resized to provide nominal arm and lower body fit, accounting for the larger HUT. Subjects held various shoulder postures (neutral posture, and various maximum shoulder rotations) with the suit pressurized to 4.3 PSI. For each posture, subjects reported the intensity of their perceived contact with the suit on a Borg CR10 scale, 0 indicating a non-existent contact intensity, and 10 representing a maximal contact intensity. Ratings were recorded for 11 regions covering the upper body and these ratings were used to determine the impact of body shape and size on suit contact. Our preliminary findings suggest that oversized HUTs had less contact regions than compared to the nominal HUT; and the region of contact, contact patterns, and contact intensities varied from person to person with the oversized HUT. This variance could be linked to subjects’ body shape and size. The next goal is to establish a causal factor between body shape and size, pose, contact region, and the HUT size. The outcome is expected to help understand the overall contact trends and insights into the influence of HUT sizing on suit fit.

Will Green↗
NASA NTRS2024

Development of Weigh-out Process and Evaluations for Underwater Partial Gravity Simulations

For the upcoming Artemis lunar missions, astronauts will need to train in a spacesuit where partial gravity can be simulated such as the NASA Neutral Buoyancy Lab (NBL). At the NBL, dive weights and foam can be added around the spacesuit to attain a satisfactory center of buoyancy (CB) and center of gravity (CG) location to simulate the lunar gravity (1/6thG) effects. If CG and CB are not co-located properly, incorrect righting moments can be introduced, and both simulation quality and EVA task performance can be impaired. Based on the findings from the initial testing using xEMU spacesuits, it was observed that the weigh-out method (i.e., determination of the weights and foam quantities and position) needed further development to improve the simulation quality, especially for the subjects who experienced excessive instability. This paper aims to present the on-going effort to improve the weigh-out process for enabling NBL lunar EVA simulations. For this effort, a human-suit model was created to use suit CAD and 3D human body scans to estimate both CB and CG location for each suited subject. NBL weigh-out testing was performed to characterize the effects of CG and CB positioning, in which the 3D human-suit model was used to determine optimal weigh-out combinations of weights and foam. Postural, balance, and subjective feedback were gathered for each weigh-out configuration. The results indicated that, as the CB was shifted higher and the CB and CG located closer to each other, the subject tended to be more stable and their EVA performance improved. A high CB location was then prioritized across 4 additional subjects in both small and large size spacesuits. When compared to the initial xEMU test series, improved performance was observed across all subjects as the CB moved higher and aligned closer to the system CG.

Pouyan Sabahi↗
NASA NTRS2024

Development of Weigh-out Process and Evaluations for Underwater Partial Gravity Simulations

For the upcoming Artemis lunar missions, astronauts will need to train in a spacesuit where partial gravity can be simulated such as the NASA Neutral Buoyancy Lab (NBL). At the NBL, dive weights and foam can be added around the spacesuit to attain a satisfactory center of buoyancy (CB) and center of gravity (CG) location to simulate the lunar gravity (1/6thG) effects. If CG and CB are not co-located properly, incorrect righting moments can be introduced, and both simulation quality and EVA task performance can be impaired. Based on the findings from the initial testing using xEMU spacesuits, it was observed that the weigh-out method (i.e., determination of the weights and foam quantities and position) needed further development to improve the simulation quality, especially for the subjects who experienced excessive instability. This paper aims to present the on-going effort to improve the weigh-out process for enabling NBL lunar EVA simulations. For this effort, a human-suit model was created to use suit CAD and 3D human body scans to estimate both CB and CG location for each suited subject. NBL weigh-out testing was performed to characterize the effects of CG and CB positioning, in which the 3D human-suit model was used to determine optimal weigh-out combinations of weights and foam. Postural, balance, and subjective feedback were gathered for each weigh-out configuration. The results indicated that, as the CB was shifted higher and the CB and CG located closer to each other, the subject tended to be more stable and their EVA performance improved. A high CB location was then prioritized across 4 additional subjects in both small and large size spacesuits. When compared to the initial xEMU test series, improved performance was observed across all subjects as the CB moved higher and aligned closer to the system CG.

Pouyan Sabahi↗
NASA NTRSDate not supplied

Virtual Fit Assessment: Validation using Historical Spacesuit Fit Data

Virtual fit tests using 3D body scans have provided a cost-effective means to predictively assess spacesuit fit for the current and future astronaut population. However, fit is a complex issue influenced by physical interferences, subjective preference, and many other factors. Fit can substantially differ between the type of hardware, environmental conditions, and tasks being performed. Namely, fit changes across different contexts, such as 3D printed mockup evaluations, pressurized one-g suited test events, neutral buoyancy and other training events, and flight extravehicular activities (EVA). These challenges make it difficult to validate virtual fit frameworks to physical suit sizing. This study proposes a new method of validation using the wealth of historically archived suit fit data. Physically Assessed Fit (PAF) data was assessed from astronauts and test volunteers who wore the legacy Extravehicular Mobility Unit (EMU). The data was collected during the past decades at NASA and considered to be most reliable and dependable. Each participant was 3D scanned for body shape and spacesuit-critical dimensions were measured per NASA guidelines. The participants’ size preference for hard upper torso (HUT) assembly was retrieved from their pressurized suit fit check records. Fit assessments were also updated after one-g EVA training, neutral buoyancy training, and EVA flights. Virtually Assessed Fit (VAF) was done by overlaying the CAD model of a HUT with the 3D body scans of the PAF participants. Each body scan was iteratively adjusted for the position inside the HUT to minimize the suit-to-body contact interference while satisfying a set of prescribed requirements. Then the residual contact area, depth, and volume were quantified as VAF metrics. This process was repeated for the different HUT sizes, including medium, large, and extra-large. Statistical modeling is currently in progress and will be presented at the conference. A statistical classifier will be developed to predict a fit probability for different HUT sizes as a function of the corresponding person’s VAF metrics. The HUT size with the highest fit probability will constitute the most likely size selection for the person, and the prediction will be compared against the corresponding PAF data. The participant data will be randomly pre-grouped into either a model development or validation subset. While the model development subset will be used to build the probability model, the validation subset will be used to assess the model accuracy. Also, the locations and magnitudes of suit-body-contacts will be estimated from VAF. This information can identify the critical suit geometry and body shape features that influence suit fit. The variations found in PAF size selections by subjects with similar VAF metrics will allow for investigations into subjective preferences, for example, tight versus loose fit. Overall, this study is expected to provide a structured validation of a virtual suit fit framework, which has not been possible in the past. The outcome can also provide useful insights and potential limitations for interpreting virtual fit tests for future spacesuit designs and population accommodation.

Han Kim↗
NASA NTRSDate not supplied

xEMU Padding Optimized for Body Mobility in Lunar EVAs

We developed a new design framework for padding in the xEMU hard upper torso (HUT) that will optimize the crewmembers’ mobility and comfort. The new padding was geometrically optimized for anthropometric body curvatures and motion dependent body deformations.

Yaritza Bernal↗
NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. This was expected to accelerate development and provide more cost-effective, time-saving solutions. This work was selected for a NASA Crowdsourcing project through an agency-wide solicitation. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation and an execution crowdsourcing platform partner to solicit framework developments from external contenders. NASA provided contenders with video clips of spacesuits and simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy (weighted combination of scoring metrics). Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA simulation environments such as the Neutral Buoyancy Lab (NBL). However, 3D joint identification is less reliable when parts of the suit were obstructed in the image. After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗
NASA NTRSDate not supplied

EVA Suit Evolution

Explore the source record for details and available documents.

Sudhakar Rajulu↗
NASA NTRSDate not supplied

Modeling Spacesuit-Human Interaction for Injury Risk Identification

Performing functional tasks while inside of the space suit introduces additional ergonomic challenges which can lead to musculoskeletal stresses and injuries in astronauts. Computational modeling of the suit-body interaction can be used alongside human-in-the-loop testing to understand and mitigate these risks and potentially improve the work performance. In this work, a static rigid-body model of the human body was integrated with the space suit and the process is described. This model enables prediction of the resulting joint torques and musculoskeletal loading from a simulated extravehicular activity (EVA) task. For representative EVA tasks, joint torque and body angles were used as parameters to predict the muscle strength required to achieve the task, as well as the percentage of the general and astronaut-like population that could achieve that strength demand. Population strength capability can be used as an indicator of the difficulty of a task and the level of injury risk for the given task types and crew anthropometry. The sensitivity of the model to inputs such as body joint location within the space suit and individual anthropometry will also be evaluated. Future work will use a database of3D human body scans and the movements of the body inside the suit during different EVA tasks to identify potential soft tissue contact points. The specific locations and magnitudes of contacts between the body and suit are expected to identify risk of repetitive tissue contact stresses. Overall, this model will provide a new tool to structurally identify the injury risks of EVA tasks and evaluate alternate strategies to reduce injury risk.

Garima Gupta↗
NASA NTRSDate not supplied

Spacesuit and Mobility Performance Changes

The complex interactions between the human body and spacesuit lead to changes inmovement patterns and mobility performancesof the wearer. In general, factors including the geometric properties, such as shape and size, mechanical properties of the suit, and pressurization of the suit are known to be associated with altered movement patterns as compared to an unsuited human.However, their relative contributions have not been explicitly quantified from the mobility performance perspectives. The goal of this study wasthus to assessthe effects fromthe different types of mobility constraintconditions, namely by wearing either a 3D printed hard upper torso (HUT) assembly orfully pressurized spacesuit. The outcome was also compared against the unsuited motions. For this study, an xEMU (exploration Extravehicular Mobility Unit) suitwas considered, which is the next generation spacesuit developed by NASA. In each test condition, the subject was asked to move the arm and hand as prescribedfor different task types, and the corresponding body segment locations were recordedusing a 3D motion capture system. The following three tasks were performedand analyzed: 1) Outward one-handed reaches:the subject in a standing pose made sweeping motions with the extended right arm from the extreme end-to-end positions, including side-to-side at different elevations and top-to-bottom at different azimuths. 2) Outward two-handed reaches:similarto the previous task, howeverthe subject kept the hands together during the motions in order to assessthe areas that can be reached by both hands. 3) Inward one-handed reaches:the subject made right-hand reach motions to the surface of the HUT.The hand traces collected from each task were modeled by a template shape parametrically deformed with a radial basis function. This process enabled foran abstraction of the hand traces into a smooth surface envelope representing the maximally reachable area of the test subject, of which the shapes and sizes were compared across the different test conditions. The preliminary analysis has shown that theoverall size of reachenvelopes decreases in a pressurized suit compared to 3D printed mockup HUT and unsuited conditions. The specific shape of the envelopes, which were determined by the reachable and unreachable zones, alsovary with the testconditions, and the differences werepronounced with the inward reaches to the HUT surface. The latter observation ispotentially relatedto the increased demandfor shoulder and elbow flexions.Overcomingthe resistance from the pressurizedsoft goods and mechanical constraints of the shoulder assemblywas seen to be associated with the difference in motion patterns between the suited and unsuited conditions. Overall, the information quantified from this study is expected to provide structured metrics for spacesuit mobility, which can improve design optimization and human-system integration.

K Han Kim↗
NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Preliminary work into this field was promising but given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation (CoCEI) and an execution crowdsourcing platform partner to solicit machine learning framework developments from external contenders. NASA provided contenders with images and video clips of spacesuits with simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, the top five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The weighted scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy. Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA environments such as the NASA Active Response Gravity Offload System (ARGOS). After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗
NASA NTRSDate not supplied

Toward an IMU-based Space Suit Motion Capture System

Spacesuits are complex engineering systems that sustain human health and enable performance outside of Earth-like environments. These systems must support human mobility and physical workload demands while minimizing injury risk during extravehicular activity (EVA). Future EVA on the lunar surface during the Artemis program is expected to be more frequent and require higher physical workloads than previous EVAs during the ISS, Shuttle, or Apollo programs. Hence it is important to optimize future as well as current spacesuits to be efficient and comfortable for the success of space and planetary missions. To enable this, an efficient method is needed to test these spacesuits on the ground.When testing spacesuits in ground environments, it is often necessary to understand the kinematics of the suit to validate the design against relevant requirements or characterize the physical workload necessary to operate the suit. This is a challenging task for traditional optical motion capture (OMC) approaches: suit-mounted OMC markers are easily occluded by the subject or environment and may become detached during testing. Controlling lighting and reflectivity of objects in the motion capture volume is also difficult. Fixed-position OMC cameras also constrain testing to a small and contrived laboratory environment, disallowing kinematics capture in field environments.One promising alternative is the use of suit-mounted inertial measurement units (IMUs). These sensors are small, unobtrusive, and portable, but come at the cost of increased sensor noise and complexity of the software and mathematics to analyze the collected data. To this end, engineers at NASA are developing the Augmented Suit Inverse Kinematics (ASIK) system, a complete motion capture methodand inverse kinematics solver which relies solely on a network of wireless IMUs attached to the major kinematic segments of the spacesuit. The ASIK modeling language allows for the simple inclusion of probabilistic priors such as suit size and shape or IMU positions and rotations. Furthermore, to increase accuracy and reduce operational overhead to use this motion capture approach, the developed inverse kinematics solver exploits so-called self-calibratingalgorithmic techniques, which reduce the need for precise alignment of the sensors on the segments or scripted functional calibration procedures. The ASIK system was tested in a 7-subject pilot study. Each subject donned NASA’s new prototype exploration spacesuit in the Active Response Gravity Offload System (ARGOS) facility at the NASA Johnson Space Center. The subjects were outfitted with a set of 14 APDM (Portland, OR, USA) Opal IMUs, 12 of which were used in the ASIK model to estimate lower body and trunk kinematics. The subjects were also outfitted with a set of reflective OMC markers and traditional OMC data was collected and processed. Presented results will include characterization of ASIK-derived suit joint angles accuracy against an optical motion capture datum. Discussion of these results, as well as discussion of system calibration and nuances of mathematical observability, will be included.If successful, IMU-based motion capture will enable testing and validation of spacesuits more frequently, with less overhead, in more extreme environments. Future work will apply these techniques to common spacesuit testing tasks, such as gait, mobility, and balance assessment, physical workload characterization, and ergonomics evaluations.

Timothy Mcgrath↗