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Human modeling tools for spacesuit and hardware design and assessment
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Human modeling tools for spacesuit and hardware design and assessment K. Han Kim1, Karen Young1, Elizabeth Benson2, Sarah Jarvis2, Linh Vu2, Yaritza Hernandez3 and Sudhakar Rajulu4 1
Leidos, Houston, TX, United States; 2MEI Technologies, Inc., Houston, TX, United States; 3KBRwyle, Houston, TX, United States; 4NASA Johnson
Space Center, Houston, TX, United States
1. Introduction During the early NASA programs in the 1960s, such as Gemini and Apollo, the astronauts were all males largely recruited from the military. Thus, crewmembers generally had similar body shapes and dimensions (Fig. 46.1A). However, astronauts later selected for future programs consisted of a population that included both men and women with diverse anthropometry (Fig. 46.1B). The diversified anthropometry of the astronauts raises an unprecedented challenge for fit and accommodation of the spacesuit and hardware. The spacesuit in particular is a unique component of spaceflight of which the development and design are significantly influenced by anthropometry. Spacesuit design is an outcome of multifaceted goals and requirements. A suit should first provide essential protection from environmental hazards including vacuum environment, micrometeorite, and extreme thermal conditions. Next, the specific context of extravehicular activity (EVA) should be considered, such as microgravity tasks or terrain exploration use. The design then needs to ensure the fit of the target population by minimizing performance restrictions and maximizing the comfort. Furthermore, owing to the bulkiness and mass of the suit (up to 140 kg including the life support system), the maintenance and supporting hardware, storage, and logistics also should be considered for suit design. Suit fit is not just an issue of comfort as the performance and mobility tend to degrade with suboptimal suit fit. Suboptimal suit fit can result in pain or injury during EVA and ground training. A notable concern is a potential risk of shoulder injuries to crewmembers due to the limitations in shoulder mobility of the hard upper torso (HUT) assembly (Williams & Johnson, 2003). It has been reported that insufficient clearance between the shoulder and the scye-bearing joint may result in restriction of scapulothoracic motion (Fig. 46.2), possibly leading to injury.
2. Anthropometry for suit design and fit Suit design and fit techniques have been progressively improving throughout the decades of spaceflight. In particular, fit for a diverse population and reducing cost have been gradually playing a more important role. NASA Man-Systems Integration Standards (National Aeronautics and Space Administration, 1995) and Space Flight Human System Standards (National Aeronautics and Space Administration, 2015) indicate that anthropometric data should be considered at hardware design, with particular attention to the smaller female crewmembers. The specific phases are outlined as follows.
2.1 Apollo suit: custom fit During the early space programs, including Apollo in the 1960s, spacesuits (Fig. 46.3A and B) were designed to be single-mission-use garments. Thus, spacesuits were custom-built to each individual astronaut’s body dimensions, which was possible with the small number of crewmembers. For example, the torso limb suit assembly (Fig. 46.3C) was custom sized,
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FIGURE 46.1 (A) Mercury Program crewmembers with similar anthropometry in the 1960s. (B) Diverse gender and anthropometry of Space Shuttle crewmembers in the 2000s. Photographs from NASA.
FIGURE 46.2 Restricted clearance between the shoulder and scye-bearing joint in an overhead reach motion (Williams & Johnson, 2003).
and the limb portions were graduated in size and made adjustable to accommodate individual crewmember’s limb lengths. Astronauts also had the opportunity to undergo multiple suit-fit checks to ensure optimal fit and desired performance of the suit. Each astronaut had three custom suits, including a flight suit, training suit, and backup flight suit.
2.2 Extravehicular mobility unit: modular design based on linear dimension measurements As more diverse crews were selected for spaceflights (for example, about 120 astronauts participated in the Shuttle Program), individually customized suits became cost-prohibitive to accommodate a wider variation in dimensions. In the 1980s, a new suit called the Shuttle Extravehicular Mobility Unit (EMU) was developed and exclusively used during the Shuttle and the International Space Station (ISS) EVA missions (Fig. 46.4A). The new Shuttle EMU had significant improvement over the Apollo suit with the addition of the shoulder bearings and waist seal enclosures. In addition, as opposed to the custom-fit Apollo suits, the Shuttle EMU was intended to furnish components in modular sizes, such as extra small, small, medium, large, and extra large, for the torso section of the suit and several arm, waist, brief, and leg sizes. With all of these adjustable size combinations, the Shuttle EMU was intended to accommodate an astronaut population from a fifth percentile American female to a 95th percentile American male according to the US Air Force
(A) Apollo A-6L spacesuit. (B) Apollo A-7L. (C) Apollo extravehicular torso limb suit assembly (TSLA). Photographs from NASA.
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FIGURE 46.4 (A) Extravehicular Mobility Unit (EMU). (B) Linear anthropometry measurements. Photograph from NASA. Images from NASA-STD3000.
measurements (National Aeronautics and Space Administration, 2000). Based on the hardware produced during the EMU program, the actual anthropometric range that could adequately fit in the EMU ranged from a 40th percentile female to a 95th percentile male. The appropriate suit component sizing for each crewmember was based on the linear and circumferential measurements of body shapes, including stature, inter-scye breadth, bi-deltoid breadth, vertical trunk diameter, and other critical dimensions (Fig. 46.4B). An algorithm was developed for Shuttle EMU sizing to predict preliminary suit component sizes that combine to make a full suit for each crewmember (NASA, 2015). Crewmembers then performed a physical fit check to evaluate the suit fit.
2.3 Z-2: 3D scan and print technology The Z-2 prototype suit (Fig. 46.5A), which was delivered to NASA in 2016, was designed using the 3D body scans of targeted subjects in various poses. The 3D geometry-based technique initially was tested on the EMU HUT fit validation assessments and was fully deployed for Z-2 development using a series of strategically selected multiple body scans. The suit fit was verified from the early development stage by overlaying body scans with the computer-aided design (CAD) drawings of the suit (Fig. 46.5B). It was hypothesized that penetration and overlap between the suit and body shape geometry, if it exceeds a threshold level, can result in discomfort, pain, and kinematic interferences. Furthermore, a clearance should exist between the suit and body surface to accommodate the morphologic variations of the skin and
FIGURE 46.5
(A) Z-2 prototype suit. (B) Design of the Z-2 using 3D scans in various poses (Ross et al., 2014). (A) Photograph from NASA.
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muscles during motions, although differentiating between interference and contact required to move the suit still remains to be addressed. To validate the virtual fit assessments, a full-scale 3D print prototype of the HUT, waist, and brief hardware components were fabricated and tested with participants. The new design process was considered to be substantially more cost-effective and time-saving than the traditional iterative designs (Ross et al., 2014). The Z-2 was designed to enhance the accommodation of smaller crew population and incorporated inter-shoulder bearing distance adjustments for optimal fit and improved mobility of the shoulder. With the Shuttle EMU, although the baseline version had a small-size HUT and a prototype of an extra-small HUT, the Enhanced EMU, which is the upgraded version that has been used on the ISS, lacks the small and extra-small sizes. Also, the Z-2 suit features rear-entry architecture for easier donning/doffing, as opposed to the waist-entry architecture for the EMU. Previously, EMU donning by waist-entry method in particular with the planar HUT was identified as a probable risk factor for shoulder injury (Williams & Johnson, 2003) as it required some wearers to make a series of arm and shoulder movements to the extremes of their range of motion (ROM).
2.4 Z-2.5: Monte-Carlo fit assessment The next prototype suit currently in development is the Z-2.5. The main goal of Z-2.5 was to reduce the overall suit dimensions for hardware compatibility and stowage/worksite volume decrease without impacting the anthropometric ranges for the current and future crew populations, in particular for smaller-size females. Thus, the design needed to be optimized and evaluated to achieve the multiple goals and constraints. In addition, the comfort and performance of the crew are an important design goal. The fit of the Z-2.5 design concept has been simulated and evaluated using a virtual fit assessment technique from the initial development stage. The outcome of the virtual assessment is planned to be validated using the 3D print mockup similarly with Z-2 and further using the actual suit upon delivery. This technique is a step forward from the boundary manikin method adopted for the Z-2 design. With the boundary manikin technique, a small number of 3D body shape samples were considered. Generally, the sampled body shapes correspond to the extreme ends of target measurement dimensions, such as very large or small stature or body mass index (Fig. 46.6A). The sampled body shapes then were tested against the 3D CAD drawing of the suit for potential contact and clearance check. If the contact areas or overlap volumes were below the set threshold, the hardware or suit was assumed to accommodate the population segment (e.g., 90% or 95%). Also, the fit boundary, by which the fitting versus unfitting cases are separated (dotted circle in Fig. 46.6A), was hypothetically approximated (i.e., interpolated or extrapolated) from the arrangement of the boundary manikins in the anthropometry measurement space. The boundary manikin technique provides a more reliable estimation of the accommodated population than using a very small number of scanned body shapes that may not accurately represent the entire target population. However, the limitation is that the fit boundary is a hypothetical approximation based on the sparse samples, and the body shapes within or beyond the hypothetical boundary are not explicitly tested for fit. Thus, the estimated segment of accommodated
FIGURE 46.6 (A) Boundary manikin fit assessment method. (B) Monte-Carlo fit assessment method.
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population can vary substantially depending on the selection, availability, and the overall distribution of the sample manikins. A new technique was proposed based on the Monte-Carlo type of fit assessments. While the boundary manikin technique is a series of tests using worst case conditions, the Monte-Carlo assessment is based on a large number of body shapes, which can be either 3D scanned (Gordon et al., 2014; Margerum, Ferrer, Young, & Rajulu, 2010) or statistically generated (Azouz, Rioux, Shu, & Lepage, 2006; Kim et al., 2016; Wuhrer & Shu, 2013; Zhou, Sun, Roos, Li, & Corner, 2016). The sample body shapes cover a large area of the target anthropometry space (Fig. 46.6B). Each body shape will be tested against the CAD drawings to provide a ‘fit’ or ‘unfit’ decision. While the fit test method may be essentially similar to the boundary manikin method, a key difference is that the assessments are automated given the large number of tests required to be performed across the broader population ranges. Specifically, each manikin is iteratively positioned inside the CAD suit geometry to search for an optimal position, which minimizes the suit-to-body surface overlap and also simultaneously satisfies a set of prescribed requirements, such as the top of the shoulder maintaining a preset clearance from the scye ring, the arms being able to extend straight forward from the shoulder joint through the scye opening, and the head approximately centered within the helmet. With the manikin optimally positioned within the suit, the penetration depth of the suit into the manikin, contact areas, and overlap volume were geometrically quantified (Fig. 46.7). The quantified overlap metrics were compared to a set of threshold levels for a fit evaluation. Once fit is evaluated for each body shape, the proportion of accommodated population is estimated by counting the fitting versus unfitting cases. The benefit of the Monte-Carlo technique is that the proportion of population fit is not approximated from the sparse set of boundary manikins. Rather, the proportions are explicitly quantified from the dense samples. More importantly, the accommodation boundary can be directly identified by grouping the manikins confirmed to be accommodated from the fit test (shaded ellipsoid in Fig. 46.6B). The marginally fitting cases (represented by the gray band boundary surrounding the accommodated cases in Fig. 46.6B) provide useful information to locate design issues, such as specific contours of the suit components or body segment shapes that can lead to a restricted clearance or unwanted overlap. Furthermore, the same technique can be applied to multiple sizes or configurations of suits (Fig. 46.6B). The outcome of multilayer analyses can identify the specific directions and magnitudes of the changes in population accommodation when an alternate suit size or configuration is selected. In other words, a prediction can be made on how many more or less people can be incrementally accommodated by switching from one suit type to another suit type or which body segments are more sensitively influenced in overlap volume if an additional size suit was used, etc. Overall, it is expected that the new technique will provide more direct assessments of suit fit and accommodation than traditional methods and can be used for design improvements and decisions. However, it should be noted that anthropometric variations alone may be insufficient to explain the probability of the suit fit versus unfit for the entire crew. For example, individual preferences, which in general play an important role in individual suit fit judgment, have not been considered in this virtual fit assessment method. To improve the prediction accuracy, the virtual fit assessment needs to be complemented with other types of models, such as comfort/discomfort perception and individual tolerance for tissue compression. These models can parametrically adjust the thresholds of the penetration and overlap to improve the overall prediction accuracy.
FIGURE 46.7 (A) Suit-to-body contact and penetration depth calculation. (B) Overlap area and volume assessment between the suit and body.
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3. Body geometry changes in microgravity Another issue related to spaceflight anthropometry is body shape changes in microgravity. The changes are associated with a spinal elongation that straightens the spinal curvature (Brown, 1975, 1977; Churchill, Laubach, Mcconville, & Tebbetts, 1978; Thornton, Hoffler, & Rummel, 1977; Thornton & Moore, 1987). One of the issues caused by spinal elongation is the change in suit fit. Based on the reports from some crewmembers experiencing difficulty donning suits after extended periods in microgravity, 2.54 cm is currently added to suit torso length as a sizing adjustment to account for microgravity effects (Thornton et al., 1977). A study on ISS crewmembers confirmed anthropometric changes in spaceflight, based on measurements collected using 3D photogrammetry and traditional tape measures. The measurements showed that stature increases by a maximum of 3% within the first 15 days of the spaceflight (Fig. 46.8). This increase corresponds to an observed maximum change of 4.7 cm and an average change of 2.4 cm (standard deviation: 1.3 cm) in stature. After the initial increasing phase, the crewmembers showed a slight decrease and maintained a steady phase throughout the duration of the mission. Postflight measurements showed stature decreases to a level similar to preflight. Other measurement dimensions also showed changes in microgravity. For example, hip and thigh circumference tend to decrease during spaceflight down to 7% and 10%, respectively, compared with preflight measurements. The anthropometry changes in spaceflight also were visualized with a 3D parametric body modeling technique. The model was developed through dimensional reduction and regression analyses from 3D scans. Each scan was normalized for posture (Danckaers et al., 2018). The preflight scans of individual crewmembers were parametrically modified and updated using the in-flight and postflight measurements (Fig. 46.9). It is expected in the future that the model geometry can be tested for suit fit and the differences of suit-to-body contact can be predictively quantified.
4. Suit mechanical limit and human-in-the-loop simulation Suit design is a highly iterative process and usually involves the creation of multiple prototypes with associated human-inthe-loop testing. To streamline this process, there has long been an interest in modeling spacesuit performance and determining how small changes in suit design can affect that performance. In general, due to the mechanical constraints, weight, and the stiffness caused by pressurization of the spacesuit, the mobility and performance of the wearer tend to decrease when compared to an unsuited condition. For example, the average ROM for shoulder flexion extension in an unsuited condition (166 degrees) decreases to 139 degrees on average while wearing a pressurized Mark III EVA suit
FIGURE 46.8 Stature changes in spaceflight measured before flight, on flight day 15 (FD 15), flight day 80 (FD 80), 15 days before return (FD R-15), and after flight. Each line denotes the measurements from individual crewmembers.
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FIGURE 46.9 Parametric modeling of anthropometry changes in spaceflight. (A) Preflight (blue wireframe) versus in-flight (gray blob) (dark gray in print version). (B) In-flight (gray blob) versus postflight (red wireframe) (light gray in print version).
prototype (England et al., 2012). Shoulder abduction strength decreases by 18% when working in a pressurized (29.6 kPa) Shuttle EMU with a planar type HUT compared with an unsuited condition (Amick, Reid, England, & Rajulu, 2015). Thus, an essential prerequisite for building human kinematic and dynamic models for interactions with a spacesuit is to define the ROM and other kinematic restrictions inherent in the suit design. The shoulder ROM of a suited person is primarily determined by the mechanical joint limit at the scye rings and pressure bladder/restraint convolutes in the shoulder region, in addition to individual specific musculoskeletal limitations. Furthermore, the interactions between the body and the suit, which vary with the body shape and position within the suit, can substantially change the shoulder ROM. To simulate the interactions between the suit mechanism and the wearer’s body, the shoulder ROM of the planetary exploration suit was evaluated in 3D CAD. For the mechanical work envelope estimation, scye ring and shoulder ring convolutes were manipulated into their maximally rotated positions while the corresponding reach envelopes were created by the traces of the “virtual upper arm”, which was constrained at the center of the convolute (Fig. 46.10A). The shape of the reach envelope started from a half-sphere primitive but was programmatically deformed by the mechanical
FIGURE 46.10 (A) mechanical work envelope for PXS suit. (B) Mechanical work envelope overlaid with a wearer’s reach envelope (blue trace highlighted by green envelope). PXS, planetary exploration suit.
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interferences, such as the points where the hardware could no longer be moved due to the collision with the helmet ring or other suit components. The mechanical work envelope was compared with the reach envelope approximated from a suited test participant’s arm sweep motion (Fig. 46.10B). It was observed that the overall shapes are different between the reach envelopes. The wearer’s motion envelope showed a shape shrink in directions toward the upper and rear regions. Medial reach regions also are restrained in the wearer’s motion. The surface areas of the envelopes are 0.27 m2 for the mechanical work envelope and 0.25 m2 for the envelope approximated from the wearer’s motion. It is hypothesized that the shape differences are related to the stiffness of the soft goods around the arm, which create a resistance to the arm motions. Furthermore, certain suit components contacting with the wearer’s arm and shoulder can act as pressure points that further restrict the shoulder ROM. Overall, modeling shoulder kinematics and geometry interacting with the suit is of importance, given the risk of shoulder injury from restricted suited motions and the complexity of the skeletal system and musculature at the shoulder. One of the limitations of this simulation is related to the virtual upper arm artificially constrained at the center of the convolute. At the bounds of the simulated ROM, the arm may actually move further out until the arm makes a contact with the inner surface of the convolute. Arm motions can be made even beyond the hard stop created by the hardware to the extent at which the skin and tissue compression can be maximally tolerated. The collapsing of the soft goods can also enhance the ROM, depending on the level of the force the wearer exerts. Furthermore, the simulation assumes that the upper body is stationary during the arm motions. However, the torso is likely to shift during arm motions due to arm reaction forces. These limitations necessitate the quantification of the body contact and motions within the suit, the information of which has been difficult to measure due to the sensor and other issues (details are discussed later in this chapter).
5. Suited mobility assessments Maximum reach envelopes are commonly used in ergonomics to assess the mobility of a person and to ensure that critical controls and hardware are placed within reach. A reach envelope provides a metric for spacesuit design to assess the suit performance and the design of EVA tasks. As discussed previously, a pressurized spacesuit generally induces a substantial reduction in reach dimensions and other geometric variations compared with an unsuited condition. However, the specific patterns of suited kinematics should be ideally quantified in microgravity or a close analog, such as an underwater environment, but the cost can be prohibitive. Thus, a low-cost motion-capture system was developed for underwater testing based on video images recorded from multiple cameras to triangulate marker coordinates. Four commercial off-the-shelf cameras (GoPro Hero 4) were mounted around the capture volume (Fig. 46.11A) after each camera was calibrated to correct for optical distortions, by capturing
FIGURE 46.11 (A) Motion capture volume setup. (B) Isolated arm motions. (C) Whole-body reach motions.
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the images of a checkerboard and matching the images with the known geometry of the checkerboard pattern. The location and orientation of each camera with respect to one another was determined using a global coordinate reference frame (1.7 2.3 2.0 m frame with 32 spherical markers). The system performance was validated on dry land, and an accuracy of 1.9 cm root mean square error was observed. The system was deployed at the Neutral Buoyancy Laboratory at NASA Johnson Space Center. During testing, a suited subject performed sets of prescribed reach motions, including isolated arm motions (arm sweeps at the shoulder: Fig. 46.11B) and whole-body reach motions (Fig. 46.11C). While performing reach motions, the subject held a wand that had dive lights within ping-pong ball diffusers attached to each end. The lights were used to improve marker detection and labeling. A marker-tracking algorithm was implemented using an open-source software program including a computer vision library OpenCV (OpenCV, 2017) and a 3D geometry authoring tool Blender (Blender Foundation, 2017). The goal of the work was to compare the kinematic performances represented in the reach envelopes between the EMU and Z-2 suits. The results from five subjects demonstrated that overall shapes of the isolated hand-arm reach envelopes are similar between the Z-2 and EMU (Fig. 46.12). However, the Z-2 suit shows envelopes that are stretched further backward compared with the EMU, with corresponding depth measurement increase of 17 cm, on average. The intersection width and the area between the left and right hand envelope are over three times larger with the Z-2, which indicates a substantially increased capability for cross reaches. The whole-body reach envelopes showed similar results to the isolated arm-hand motions (Fig. 46.13). However, the volume of reach envelopes demonstrated a 25% increase with the Z-2 compared with the EMU. Also, the intersection between the left and right hand envelopes are more than 3.4 times larger with the Z-2 than with the EMU. The increased reach volume and cross-reach capability in the Z-2 is potentially a result of the enhanced joints and soft goods mobility of the upper and lower torso assembly.
6. Kinematics and body geometry inside the spacesuit While the kinematics and geometry of the spacesuit provide important information for suit and hardware design, the wearer’s body shape and kinematics inside the spacesuit should be measured to assess the biomechanical and contact stresses on the body. The challenge with such measurements is that a spacesuit prevents most kinematic evaluation techniques (optical motion capture, inertial measurement units, etc.) due to visual obstruction, space restrictions for devices attached to the body, and ferrous magnetic interference. To assess body motion and suit-to-body contact inside the suit, a wearable sensor garment was developed based on low-profile, fabric stretch sensors. A fabric strain sensor yields a capacitance variation in response to the sensor elongation and deformation. To build a sensor garment, different sensor configurations were first evaluated through iterative simulations. Once the optimal sensor pattern was determined, 10 stretch sensors were embedded into a tight-fitting garment, and a software tool was developed. Three-dimensional scans and corresponding sensor measurements were collected from test participants who performed several unsuited trunk postures in a 3D full-body scanner while wearing the sensor garment. Using the collected scans, a machine-learning algorithm was constructed to predict upper body shape and joint angles as a function of the strain sensor measurements. The focus was on characterizing skin deformation and shape variations for the lower back region. The results were validated against the raw scans. Initial assessments demonstrated that body shape
FIGURE 46.12 Average envelopes for isolated hand-arm reaches in Z-2 (red) (light gray in print version) and EMU (blue) (dark gray in print version). (A) Isometric view. (B) Top view. EMU, extravehicular mobility unit.
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FIGURE 46.13 Average envelopes for whole-body reach envelopes from Z2 (A) and EMU (B). EMU, extravehicular mobility unit.
and joint angles can be predicted with sensor measurements with reasonable accuracy (Fig. 46.14). Prediction errors were less than 1.8 cm and 12 degrees for estimated skin deformation patterns and joint angles, respectively. Given the reliable prediction of the body shape and poses, the next step should be that the sensor garment be examined through suited human-in-the-loop testing with a focus on sensor performance variations inside a spacesuit. EVA-like tasks can be assessed along with how the sensors perform inside the suit enclosure due to the interactions from layers of garments, including the liquid cooling ventilation garment worn under the suit. With additional development, this minimally invasive technique can potentially enable the quantifications of motions and suit-to-body interactions within the suit.
FIGURE 46.14 (A) Sensor garment system. (B) Sample postures scanned. (C) Sensor activation patterns characteristic to different lumbar postures. (D) Predicted upper body shapes from sensor signals.
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7. Conclusion As outlined in this chapter, human modeling techniques have been progressively changing the way spacesuits are designed. The traditional method of linear anthropometry has been expanded from basic geometric shapes sized with linear anthropometric measurements to 3D parametric body shape modeling. This technique has been used to predictively assess spacesuit accommodation for the current and future crew population and has been further refined with in-flight changes to body measurements. The kinematic performance of spacesuits also has been assessed to provide suit performance metrics and, in the future, to optimize EVA tasks. New wearable solutions have been developed to potentially enable the kinematic and shape measurements of the body inside the suit. Future modeling work will include the integration of body shape and kinematic measurements through a reposable and dynamic body shape model. Such models will allow the assessment of many different suit-related tasks, including donning and doffing a suit, vehicle ingress and egress, and walking, which have not been modeled using traditional techniques. Suit-to-body contact and tissue compression from the suit are important issues to be quantified as well. Although incorporating different models, such as compression tolerance, strength, and kinematic capacity, can improve the overall model accuracy, model predictions would be still associated with a certain extent of uncertainties. As previously discussed, subjective preferences still remain as one of the sources of uncertainties to be quantified. A potential solution is to treat the subjective preferences as a stochastic variable and enable human models to specify the distribution of subjective preference uncertainties when a prediction is made. Despite the difficulty of quantification, such uncertainty metrics can provide a useful tool for suit designs. For example, when the direction and range of the subjective preference variability for suit fit were identified, ancillary materials (e.g., padding, moleskin) or user-adjustment components can be designed and included to address the remaining variability. Although not included in this chapter, there are a number of areas where human modeling has been used. Human-inthe-loop assessments for vehicle control and flight hardware design are one of the areas actively adopting human modeling techniques. In addition to body shape and anthropometry changes in microgravity, body poses exhibit adaptation in spaceflight. A 3D geometry reconstruction technique has been used to simulate the neutral body poses in microgravity. Not only for spaceflight, ground and underwater analog training for astronauts has been considered for musculoskeletal and biomechanical stress assessments and visualization. Overall, human modeling is an integral process that has been making substantial influences in system design and engineering decision for spaceflight.
Acknowledgments The authors would like to acknowledge and express thanks for the valuable technical advice provided by J. Scott Cupples at NASA Johnson Space Center.
References Amick, R. Z., Reid, C. R., England, S. A., & Rajulu, S. L. (2015). Characterization of joint resistance and performance degradation of the extravehicular mobility unit spacesuit a pilot study. Proceedings of the Human Factors and Ergonomics Society e Annual Meeting, 59(1), 1259e1263. https://doi. org/10.1177/1541931215591202. Azouz, Z. B., Rioux, M., Shu, C., & Lepage, R. (2006). Characterizing human shape variation using 3D anthropometric data. The Visual Computer, 22(5), 302e314. https://doi.org/10.1007/s00371-006-0006-6. Blender Foundation. (2017). Blender e a 3D modelling and rendering package. https://www.blender.org/. Brown, J. (1975). Skylab 4 and ASTP crew height. National Aeronautics and Space Administration. Brown, J. (1977). Crew height measurement. In A. Nicogossian (Ed.), The apollo-soyuz test project medical report (pp. 119e121). Washington, DC: National Aeronautics and Space Administration. Churchill, E., Laubach, L. L., Mcconville, J. T., & Tebbetts, I. (1978). Anthropometric source book. In Anthropometry for designers (No. NASA-RP-1024, S-479-VOL-1) (Vol. 1). Houston, TX. Danckaers, F., Huysmans, T., Hallemans, A., De Bruyne, G., Truijen, S., & Sijbers, J. (2018). Full body statistical shape modeling with posture normalization. In D. N. Cassenti (Ed.), Advances in human factors in simulation and modeling (pp. 437e448). Springer International Publishing. England, S., Benson, E., Cowley, M., Harvill, L. R., Blackledge, C., Perez, E., & Rajulu, S. (2012). Comparative ergonomic evaluation of spacesuit and space vehicle design. In V. G. Duffy (Ed.), Advances in applied human modeling and simulation (1st ed., pp. 374e383). Boca Raton: CRC Press https://doi.org/10.1201/b12319. Gordon, C. C., Blackwell, C. L., Bradtmiller, B., Parham, J. L., Barrientos, P., Paquette, S. P., & Kristensen, S. (2014). 2012 anthropometric survey of U.S. Army personnel: Methods and summary statistics (No. NATICK/TR-15/007. Army Natick Soldier Research Development and Engineering Center.
Human modeling tools for spacesuit and hardware design and assessment Chapter | 46
625
Kim, K. H., Young, K. S., Bernal, Y., Boppana, A., Vu, L. Q., Benson, E. A., & Rajulu, S. L. (2016). A parametric model of shoulder articulation for virtual assessment of space suit fit. In Presented at the 7th international conference on 3D body scanning technologies (pp. 201e207). Lugano, Switzerland. https://doi.org/10.15221/16.201 Margerum, S., Ferrer, M., Young, K., & Rajulu, S. (2010). Relating linear and volumetric variables through body scanning to improve human interfaces in space. In Proceedings of the first international conference on 3D body scanning technologies (pp. 010e022). Lugano, Switzerland. https://doi.org/10. 15221/10.010 NASA. (2015). EMU sizing: Requirements and constraints (FEMU-R-005) (No. Rev G (2015) JSC-65011/CTSD-SS-2970). Houston, TX: National Aeronautics and Space Administration. National Aeronautics and Space Administration. (1995). Man-system integration Standards (No. NASA-STD-3000 (1995, Rev B)). Houston, TX: National Aeronautics and Space Administration. National Aeronautics and Space Administration. (2000). In Flight and ground system specification (space Shuttle system payload accommodations No. NSTS 07700 (Vol. X). Houston, TX: National Aeronautics and Space Administration. Retrieved from https://standards.nasa.gov/standard/nasa/nasastd-3001-vol-1. National Aeronautics and Space Administration. (2015). NASA space flight human system Standards. In Human factors, habitability, and environmental health (No. NASA-STD-3001 (2015, Rev A)) (Vol. 2)Houston, TX: National Aeronautics and Space Administration. OpenCV. (2017). Open source computer vision library. Retrieved from http://opencv.org/. Ross, A., Rhodes, R., Graziosi, D., Jones, B., Lee, R., Haque, B. Z., & Gillespie, J. W. (2014). Z-2 prototype space suit development. In 44th international conference on environmental systems. Tucson, AZ. Thornton, W., Hoffler, G., & Rummel, J. (1977). Anthropometric changes and fluid shifts. In R. S. Johnston, & L. F. Dietlein (Eds.), Biomedical results from skylab (pp. 330e338). Washington, DC: National Aeronautics and Space Administration. Thornton, W., & Moore, T. (1987). Height changes in microgravity. In Results of the life sciences DSOs conducted aboard the space shuttle 1981e1986 (pp. 55e57). Houston, TX: Space Biomedical Research Institute, NASA. Williams, D. R., & Johnson, B. J. (2003). EMU shoulder injury tiger team report (No. NASA/TM-2003-212058, S-912). National Aeronautics and Space Administration. Wuhrer, S., & Shu, C. (2013). Estimating 3D human shapes from measurements. Machine Vision and Applications, 24(6), 1133e1147. https://doi.org/10. 1007/s00138-012-0472-y. Zhou, X., Sun, K., Roos, P. E., Li, P., & Corner, B. (2016). Anthropometry model generation based on ANSUR II database. International Journal of Dental Hygiene, 1(4), 321e343. https://doi.org/10.1504/IJDH.2016.084584.
Human modeling tools for spacesuit and hardware design and assessment K. Han Kim1, Karen Young1, Elizabeth Benson2, Sarah Jarvis2, Linh Vu2, Yaritza Hernandez3 and Sudhakar Rajulu4 1
Leidos, Houston, TX, United States; 2MEI Technologies, Inc., Houston, TX, United States; 3KBRwyle, Houston, TX, United States; 4NASA Johnson
Space Center, Houston, TX, United States
1. Introduction During the early NASA programs in the 1960s, such as Gemini and Apollo, the astronauts were all males largely recruited from the military. Thus, crewmembers generally had similar body shapes and dimensions (Fig. 46.1A). However, astronauts later selected for future programs consisted of a population that included both men and women with diverse anthropometry (Fig. 46.1B). The diversified anthropometry of the astronauts raises an unprecedented challenge for fit and accommodation of the spacesuit and hardware. The spacesuit in particular is a unique component of spaceflight of which the development and design are significantly influenced by anthropometry. Spacesuit design is an outcome of multifaceted goals and requirements. A suit should first provide essential protection from environmental hazards including vacuum environment, micrometeorite, and extreme thermal conditions. Next, the specific context of extravehicular activity (EVA) should be considered, such as microgravity tasks or terrain exploration use. The design then needs to ensure the fit of the target population by minimizing performance restrictions and maximizing the comfort. Furthermore, owing to the bulkiness and mass of the suit (up to 140 kg including the life support system), the maintenance and supporting hardware, storage, and logistics also should be considered for suit design. Suit fit is not just an issue of comfort as the performance and mobility tend to degrade with suboptimal suit fit. Suboptimal suit fit can result in pain or injury during EVA and ground training. A notable concern is a potential risk of shoulder injuries to crewmembers due to the limitations in shoulder mobility of the hard upper torso (HUT) assembly (Williams & Johnson, 2003). It has been reported that insufficient clearance between the shoulder and the scye-bearing joint may result in restriction of scapulothoracic motion (Fig. 46.2), possibly leading to injury.
2. Anthropometry for suit design and fit Suit design and fit techniques have been progressively improving throughout the decades of spaceflight. In particular, fit for a diverse population and reducing cost have been gradually playing a more important role. NASA Man-Systems Integration Standards (National Aeronautics and Space Administration, 1995) and Space Flight Human System Standards (National Aeronautics and Space Administration, 2015) indicate that anthropometric data should be considered at hardware design, with particular attention to the smaller female crewmembers. The specific phases are outlined as follows.
2.1 Apollo suit: custom fit During the early space programs, including Apollo in the 1960s, spacesuits (Fig. 46.3A and B) were designed to be single-mission-use garments. Thus, spacesuits were custom-built to each individual astronaut’s body dimensions, which was possible with the small number of crewmembers. For example, the torso limb suit assembly (Fig. 46.3C) was custom sized,
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FIGURE 46.1 (A) Mercury Program crewmembers with similar anthropometry in the 1960s. (B) Diverse gender and anthropometry of Space Shuttle crewmembers in the 2000s. Photographs from NASA.
FIGURE 46.2 Restricted clearance between the shoulder and scye-bearing joint in an overhead reach motion (Williams & Johnson, 2003).
and the limb portions were graduated in size and made adjustable to accommodate individual crewmember’s limb lengths. Astronauts also had the opportunity to undergo multiple suit-fit checks to ensure optimal fit and desired performance of the suit. Each astronaut had three custom suits, including a flight suit, training suit, and backup flight suit.
2.2 Extravehicular mobility unit: modular design based on linear dimension measurements As more diverse crews were selected for spaceflights (for example, about 120 astronauts participated in the Shuttle Program), individually customized suits became cost-prohibitive to accommodate a wider variation in dimensions. In the 1980s, a new suit called the Shuttle Extravehicular Mobility Unit (EMU) was developed and exclusively used during the Shuttle and the International Space Station (ISS) EVA missions (Fig. 46.4A). The new Shuttle EMU had significant improvement over the Apollo suit with the addition of the shoulder bearings and waist seal enclosures. In addition, as opposed to the custom-fit Apollo suits, the Shuttle EMU was intended to furnish components in modular sizes, such as extra small, small, medium, large, and extra large, for the torso section of the suit and several arm, waist, brief, and leg sizes. With all of these adjustable size combinations, the Shuttle EMU was intended to accommodate an astronaut population from a fifth percentile American female to a 95th percentile American male according to the US Air Force
(A) Apollo A-6L spacesuit. (B) Apollo A-7L. (C) Apollo extravehicular torso limb suit assembly (TSLA). Photographs from NASA.
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FIGURE 46.4 (A) Extravehicular Mobility Unit (EMU). (B) Linear anthropometry measurements. Photograph from NASA. Images from NASA-STD3000.
measurements (National Aeronautics and Space Administration, 2000). Based on the hardware produced during the EMU program, the actual anthropometric range that could adequately fit in the EMU ranged from a 40th percentile female to a 95th percentile male. The appropriate suit component sizing for each crewmember was based on the linear and circumferential measurements of body shapes, including stature, inter-scye breadth, bi-deltoid breadth, vertical trunk diameter, and other critical dimensions (Fig. 46.4B). An algorithm was developed for Shuttle EMU sizing to predict preliminary suit component sizes that combine to make a full suit for each crewmember (NASA, 2015). Crewmembers then performed a physical fit check to evaluate the suit fit.
2.3 Z-2: 3D scan and print technology The Z-2 prototype suit (Fig. 46.5A), which was delivered to NASA in 2016, was designed using the 3D body scans of targeted subjects in various poses. The 3D geometry-based technique initially was tested on the EMU HUT fit validation assessments and was fully deployed for Z-2 development using a series of strategically selected multiple body scans. The suit fit was verified from the early development stage by overlaying body scans with the computer-aided design (CAD) drawings of the suit (Fig. 46.5B). It was hypothesized that penetration and overlap between the suit and body shape geometry, if it exceeds a threshold level, can result in discomfort, pain, and kinematic interferences. Furthermore, a clearance should exist between the suit and body surface to accommodate the morphologic variations of the skin and
FIGURE 46.5
(A) Z-2 prototype suit. (B) Design of the Z-2 using 3D scans in various poses (Ross et al., 2014). (A) Photograph from NASA.
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muscles during motions, although differentiating between interference and contact required to move the suit still remains to be addressed. To validate the virtual fit assessments, a full-scale 3D print prototype of the HUT, waist, and brief hardware components were fabricated and tested with participants. The new design process was considered to be substantially more cost-effective and time-saving than the traditional iterative designs (Ross et al., 2014). The Z-2 was designed to enhance the accommodation of smaller crew population and incorporated inter-shoulder bearing distance adjustments for optimal fit and improved mobility of the shoulder. With the Shuttle EMU, although the baseline version had a small-size HUT and a prototype of an extra-small HUT, the Enhanced EMU, which is the upgraded version that has been used on the ISS, lacks the small and extra-small sizes. Also, the Z-2 suit features rear-entry architecture for easier donning/doffing, as opposed to the waist-entry architecture for the EMU. Previously, EMU donning by waist-entry method in particular with the planar HUT was identified as a probable risk factor for shoulder injury (Williams & Johnson, 2003) as it required some wearers to make a series of arm and shoulder movements to the extremes of their range of motion (ROM).
2.4 Z-2.5: Monte-Carlo fit assessment The next prototype suit currently in development is the Z-2.5. The main goal of Z-2.5 was to reduce the overall suit dimensions for hardware compatibility and stowage/worksite volume decrease without impacting the anthropometric ranges for the current and future crew populations, in particular for smaller-size females. Thus, the design needed to be optimized and evaluated to achieve the multiple goals and constraints. In addition, the comfort and performance of the crew are an important design goal. The fit of the Z-2.5 design concept has been simulated and evaluated using a virtual fit assessment technique from the initial development stage. The outcome of the virtual assessment is planned to be validated using the 3D print mockup similarly with Z-2 and further using the actual suit upon delivery. This technique is a step forward from the boundary manikin method adopted for the Z-2 design. With the boundary manikin technique, a small number of 3D body shape samples were considered. Generally, the sampled body shapes correspond to the extreme ends of target measurement dimensions, such as very large or small stature or body mass index (Fig. 46.6A). The sampled body shapes then were tested against the 3D CAD drawing of the suit for potential contact and clearance check. If the contact areas or overlap volumes were below the set threshold, the hardware or suit was assumed to accommodate the population segment (e.g., 90% or 95%). Also, the fit boundary, by which the fitting versus unfitting cases are separated (dotted circle in Fig. 46.6A), was hypothetically approximated (i.e., interpolated or extrapolated) from the arrangement of the boundary manikins in the anthropometry measurement space. The boundary manikin technique provides a more reliable estimation of the accommodated population than using a very small number of scanned body shapes that may not accurately represent the entire target population. However, the limitation is that the fit boundary is a hypothetical approximation based on the sparse samples, and the body shapes within or beyond the hypothetical boundary are not explicitly tested for fit. Thus, the estimated segment of accommodated
FIGURE 46.6 (A) Boundary manikin fit assessment method. (B) Monte-Carlo fit assessment method.
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population can vary substantially depending on the selection, availability, and the overall distribution of the sample manikins. A new technique was proposed based on the Monte-Carlo type of fit assessments. While the boundary manikin technique is a series of tests using worst case conditions, the Monte-Carlo assessment is based on a large number of body shapes, which can be either 3D scanned (Gordon et al., 2014; Margerum, Ferrer, Young, & Rajulu, 2010) or statistically generated (Azouz, Rioux, Shu, & Lepage, 2006; Kim et al., 2016; Wuhrer & Shu, 2013; Zhou, Sun, Roos, Li, & Corner, 2016). The sample body shapes cover a large area of the target anthropometry space (Fig. 46.6B). Each body shape will be tested against the CAD drawings to provide a ‘fit’ or ‘unfit’ decision. While the fit test method may be essentially similar to the boundary manikin method, a key difference is that the assessments are automated given the large number of tests required to be performed across the broader population ranges. Specifically, each manikin is iteratively positioned inside the CAD suit geometry to search for an optimal position, which minimizes the suit-to-body surface overlap and also simultaneously satisfies a set of prescribed requirements, such as the top of the shoulder maintaining a preset clearance from the scye ring, the arms being able to extend straight forward from the shoulder joint through the scye opening, and the head approximately centered within the helmet. With the manikin optimally positioned within the suit, the penetration depth of the suit into the manikin, contact areas, and overlap volume were geometrically quantified (Fig. 46.7). The quantified overlap metrics were compared to a set of threshold levels for a fit evaluation. Once fit is evaluated for each body shape, the proportion of accommodated population is estimated by counting the fitting versus unfitting cases. The benefit of the Monte-Carlo technique is that the proportion of population fit is not approximated from the sparse set of boundary manikins. Rather, the proportions are explicitly quantified from the dense samples. More importantly, the accommodation boundary can be directly identified by grouping the manikins confirmed to be accommodated from the fit test (shaded ellipsoid in Fig. 46.6B). The marginally fitting cases (represented by the gray band boundary surrounding the accommodated cases in Fig. 46.6B) provide useful information to locate design issues, such as specific contours of the suit components or body segment shapes that can lead to a restricted clearance or unwanted overlap. Furthermore, the same technique can be applied to multiple sizes or configurations of suits (Fig. 46.6B). The outcome of multilayer analyses can identify the specific directions and magnitudes of the changes in population accommodation when an alternate suit size or configuration is selected. In other words, a prediction can be made on how many more or less people can be incrementally accommodated by switching from one suit type to another suit type or which body segments are more sensitively influenced in overlap volume if an additional size suit was used, etc. Overall, it is expected that the new technique will provide more direct assessments of suit fit and accommodation than traditional methods and can be used for design improvements and decisions. However, it should be noted that anthropometric variations alone may be insufficient to explain the probability of the suit fit versus unfit for the entire crew. For example, individual preferences, which in general play an important role in individual suit fit judgment, have not been considered in this virtual fit assessment method. To improve the prediction accuracy, the virtual fit assessment needs to be complemented with other types of models, such as comfort/discomfort perception and individual tolerance for tissue compression. These models can parametrically adjust the thresholds of the penetration and overlap to improve the overall prediction accuracy.
FIGURE 46.7 (A) Suit-to-body contact and penetration depth calculation. (B) Overlap area and volume assessment between the suit and body.
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3. Body geometry changes in microgravity Another issue related to spaceflight anthropometry is body shape changes in microgravity. The changes are associated with a spinal elongation that straightens the spinal curvature (Brown, 1975, 1977; Churchill, Laubach, Mcconville, & Tebbetts, 1978; Thornton, Hoffler, & Rummel, 1977; Thornton & Moore, 1987). One of the issues caused by spinal elongation is the change in suit fit. Based on the reports from some crewmembers experiencing difficulty donning suits after extended periods in microgravity, 2.54 cm is currently added to suit torso length as a sizing adjustment to account for microgravity effects (Thornton et al., 1977). A study on ISS crewmembers confirmed anthropometric changes in spaceflight, based on measurements collected using 3D photogrammetry and traditional tape measures. The measurements showed that stature increases by a maximum of 3% within the first 15 days of the spaceflight (Fig. 46.8). This increase corresponds to an observed maximum change of 4.7 cm and an average change of 2.4 cm (standard deviation: 1.3 cm) in stature. After the initial increasing phase, the crewmembers showed a slight decrease and maintained a steady phase throughout the duration of the mission. Postflight measurements showed stature decreases to a level similar to preflight. Other measurement dimensions also showed changes in microgravity. For example, hip and thigh circumference tend to decrease during spaceflight down to 7% and 10%, respectively, compared with preflight measurements. The anthropometry changes in spaceflight also were visualized with a 3D parametric body modeling technique. The model was developed through dimensional reduction and regression analyses from 3D scans. Each scan was normalized for posture (Danckaers et al., 2018). The preflight scans of individual crewmembers were parametrically modified and updated using the in-flight and postflight measurements (Fig. 46.9). It is expected in the future that the model geometry can be tested for suit fit and the differences of suit-to-body contact can be predictively quantified.
4. Suit mechanical limit and human-in-the-loop simulation Suit design is a highly iterative process and usually involves the creation of multiple prototypes with associated human-inthe-loop testing. To streamline this process, there has long been an interest in modeling spacesuit performance and determining how small changes in suit design can affect that performance. In general, due to the mechanical constraints, weight, and the stiffness caused by pressurization of the spacesuit, the mobility and performance of the wearer tend to decrease when compared to an unsuited condition. For example, the average ROM for shoulder flexion extension in an unsuited condition (166 degrees) decreases to 139 degrees on average while wearing a pressurized Mark III EVA suit
FIGURE 46.8 Stature changes in spaceflight measured before flight, on flight day 15 (FD 15), flight day 80 (FD 80), 15 days before return (FD R-15), and after flight. Each line denotes the measurements from individual crewmembers.
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FIGURE 46.9 Parametric modeling of anthropometry changes in spaceflight. (A) Preflight (blue wireframe) versus in-flight (gray blob) (dark gray in print version). (B) In-flight (gray blob) versus postflight (red wireframe) (light gray in print version).
prototype (England et al., 2012). Shoulder abduction strength decreases by 18% when working in a pressurized (29.6 kPa) Shuttle EMU with a planar type HUT compared with an unsuited condition (Amick, Reid, England, & Rajulu, 2015). Thus, an essential prerequisite for building human kinematic and dynamic models for interactions with a spacesuit is to define the ROM and other kinematic restrictions inherent in the suit design. The shoulder ROM of a suited person is primarily determined by the mechanical joint limit at the scye rings and pressure bladder/restraint convolutes in the shoulder region, in addition to individual specific musculoskeletal limitations. Furthermore, the interactions between the body and the suit, which vary with the body shape and position within the suit, can substantially change the shoulder ROM. To simulate the interactions between the suit mechanism and the wearer’s body, the shoulder ROM of the planetary exploration suit was evaluated in 3D CAD. For the mechanical work envelope estimation, scye ring and shoulder ring convolutes were manipulated into their maximally rotated positions while the corresponding reach envelopes were created by the traces of the “virtual upper arm”, which was constrained at the center of the convolute (Fig. 46.10A). The shape of the reach envelope started from a half-sphere primitive but was programmatically deformed by the mechanical
FIGURE 46.10 (A) mechanical work envelope for PXS suit. (B) Mechanical work envelope overlaid with a wearer’s reach envelope (blue trace highlighted by green envelope). PXS, planetary exploration suit.
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interferences, such as the points where the hardware could no longer be moved due to the collision with the helmet ring or other suit components. The mechanical work envelope was compared with the reach envelope approximated from a suited test participant’s arm sweep motion (Fig. 46.10B). It was observed that the overall shapes are different between the reach envelopes. The wearer’s motion envelope showed a shape shrink in directions toward the upper and rear regions. Medial reach regions also are restrained in the wearer’s motion. The surface areas of the envelopes are 0.27 m2 for the mechanical work envelope and 0.25 m2 for the envelope approximated from the wearer’s motion. It is hypothesized that the shape differences are related to the stiffness of the soft goods around the arm, which create a resistance to the arm motions. Furthermore, certain suit components contacting with the wearer’s arm and shoulder can act as pressure points that further restrict the shoulder ROM. Overall, modeling shoulder kinematics and geometry interacting with the suit is of importance, given the risk of shoulder injury from restricted suited motions and the complexity of the skeletal system and musculature at the shoulder. One of the limitations of this simulation is related to the virtual upper arm artificially constrained at the center of the convolute. At the bounds of the simulated ROM, the arm may actually move further out until the arm makes a contact with the inner surface of the convolute. Arm motions can be made even beyond the hard stop created by the hardware to the extent at which the skin and tissue compression can be maximally tolerated. The collapsing of the soft goods can also enhance the ROM, depending on the level of the force the wearer exerts. Furthermore, the simulation assumes that the upper body is stationary during the arm motions. However, the torso is likely to shift during arm motions due to arm reaction forces. These limitations necessitate the quantification of the body contact and motions within the suit, the information of which has been difficult to measure due to the sensor and other issues (details are discussed later in this chapter).
5. Suited mobility assessments Maximum reach envelopes are commonly used in ergonomics to assess the mobility of a person and to ensure that critical controls and hardware are placed within reach. A reach envelope provides a metric for spacesuit design to assess the suit performance and the design of EVA tasks. As discussed previously, a pressurized spacesuit generally induces a substantial reduction in reach dimensions and other geometric variations compared with an unsuited condition. However, the specific patterns of suited kinematics should be ideally quantified in microgravity or a close analog, such as an underwater environment, but the cost can be prohibitive. Thus, a low-cost motion-capture system was developed for underwater testing based on video images recorded from multiple cameras to triangulate marker coordinates. Four commercial off-the-shelf cameras (GoPro Hero 4) were mounted around the capture volume (Fig. 46.11A) after each camera was calibrated to correct for optical distortions, by capturing
FIGURE 46.11 (A) Motion capture volume setup. (B) Isolated arm motions. (C) Whole-body reach motions.
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the images of a checkerboard and matching the images with the known geometry of the checkerboard pattern. The location and orientation of each camera with respect to one another was determined using a global coordinate reference frame (1.7 2.3 2.0 m frame with 32 spherical markers). The system performance was validated on dry land, and an accuracy of 1.9 cm root mean square error was observed. The system was deployed at the Neutral Buoyancy Laboratory at NASA Johnson Space Center. During testing, a suited subject performed sets of prescribed reach motions, including isolated arm motions (arm sweeps at the shoulder: Fig. 46.11B) and whole-body reach motions (Fig. 46.11C). While performing reach motions, the subject held a wand that had dive lights within ping-pong ball diffusers attached to each end. The lights were used to improve marker detection and labeling. A marker-tracking algorithm was implemented using an open-source software program including a computer vision library OpenCV (OpenCV, 2017) and a 3D geometry authoring tool Blender (Blender Foundation, 2017). The goal of the work was to compare the kinematic performances represented in the reach envelopes between the EMU and Z-2 suits. The results from five subjects demonstrated that overall shapes of the isolated hand-arm reach envelopes are similar between the Z-2 and EMU (Fig. 46.12). However, the Z-2 suit shows envelopes that are stretched further backward compared with the EMU, with corresponding depth measurement increase of 17 cm, on average. The intersection width and the area between the left and right hand envelope are over three times larger with the Z-2, which indicates a substantially increased capability for cross reaches. The whole-body reach envelopes showed similar results to the isolated arm-hand motions (Fig. 46.13). However, the volume of reach envelopes demonstrated a 25% increase with the Z-2 compared with the EMU. Also, the intersection between the left and right hand envelopes are more than 3.4 times larger with the Z-2 than with the EMU. The increased reach volume and cross-reach capability in the Z-2 is potentially a result of the enhanced joints and soft goods mobility of the upper and lower torso assembly.
6. Kinematics and body geometry inside the spacesuit While the kinematics and geometry of the spacesuit provide important information for suit and hardware design, the wearer’s body shape and kinematics inside the spacesuit should be measured to assess the biomechanical and contact stresses on the body. The challenge with such measurements is that a spacesuit prevents most kinematic evaluation techniques (optical motion capture, inertial measurement units, etc.) due to visual obstruction, space restrictions for devices attached to the body, and ferrous magnetic interference. To assess body motion and suit-to-body contact inside the suit, a wearable sensor garment was developed based on low-profile, fabric stretch sensors. A fabric strain sensor yields a capacitance variation in response to the sensor elongation and deformation. To build a sensor garment, different sensor configurations were first evaluated through iterative simulations. Once the optimal sensor pattern was determined, 10 stretch sensors were embedded into a tight-fitting garment, and a software tool was developed. Three-dimensional scans and corresponding sensor measurements were collected from test participants who performed several unsuited trunk postures in a 3D full-body scanner while wearing the sensor garment. Using the collected scans, a machine-learning algorithm was constructed to predict upper body shape and joint angles as a function of the strain sensor measurements. The focus was on characterizing skin deformation and shape variations for the lower back region. The results were validated against the raw scans. Initial assessments demonstrated that body shape
FIGURE 46.12 Average envelopes for isolated hand-arm reaches in Z-2 (red) (light gray in print version) and EMU (blue) (dark gray in print version). (A) Isometric view. (B) Top view. EMU, extravehicular mobility unit.
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FIGURE 46.13 Average envelopes for whole-body reach envelopes from Z2 (A) and EMU (B). EMU, extravehicular mobility unit.
and joint angles can be predicted with sensor measurements with reasonable accuracy (Fig. 46.14). Prediction errors were less than 1.8 cm and 12 degrees for estimated skin deformation patterns and joint angles, respectively. Given the reliable prediction of the body shape and poses, the next step should be that the sensor garment be examined through suited human-in-the-loop testing with a focus on sensor performance variations inside a spacesuit. EVA-like tasks can be assessed along with how the sensors perform inside the suit enclosure due to the interactions from layers of garments, including the liquid cooling ventilation garment worn under the suit. With additional development, this minimally invasive technique can potentially enable the quantifications of motions and suit-to-body interactions within the suit.
FIGURE 46.14 (A) Sensor garment system. (B) Sample postures scanned. (C) Sensor activation patterns characteristic to different lumbar postures. (D) Predicted upper body shapes from sensor signals.
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7. Conclusion As outlined in this chapter, human modeling techniques have been progressively changing the way spacesuits are designed. The traditional method of linear anthropometry has been expanded from basic geometric shapes sized with linear anthropometric measurements to 3D parametric body shape modeling. This technique has been used to predictively assess spacesuit accommodation for the current and future crew population and has been further refined with in-flight changes to body measurements. The kinematic performance of spacesuits also has been assessed to provide suit performance metrics and, in the future, to optimize EVA tasks. New wearable solutions have been developed to potentially enable the kinematic and shape measurements of the body inside the suit. Future modeling work will include the integration of body shape and kinematic measurements through a reposable and dynamic body shape model. Such models will allow the assessment of many different suit-related tasks, including donning and doffing a suit, vehicle ingress and egress, and walking, which have not been modeled using traditional techniques. Suit-to-body contact and tissue compression from the suit are important issues to be quantified as well. Although incorporating different models, such as compression tolerance, strength, and kinematic capacity, can improve the overall model accuracy, model predictions would be still associated with a certain extent of uncertainties. As previously discussed, subjective preferences still remain as one of the sources of uncertainties to be quantified. A potential solution is to treat the subjective preferences as a stochastic variable and enable human models to specify the distribution of subjective preference uncertainties when a prediction is made. Despite the difficulty of quantification, such uncertainty metrics can provide a useful tool for suit designs. For example, when the direction and range of the subjective preference variability for suit fit were identified, ancillary materials (e.g., padding, moleskin) or user-adjustment components can be designed and included to address the remaining variability. Although not included in this chapter, there are a number of areas where human modeling has been used. Human-inthe-loop assessments for vehicle control and flight hardware design are one of the areas actively adopting human modeling techniques. In addition to body shape and anthropometry changes in microgravity, body poses exhibit adaptation in spaceflight. A 3D geometry reconstruction technique has been used to simulate the neutral body poses in microgravity. Not only for spaceflight, ground and underwater analog training for astronauts has been considered for musculoskeletal and biomechanical stress assessments and visualization. Overall, human modeling is an integral process that has been making substantial influences in system design and engineering decision for spaceflight.
Acknowledgments The authors would like to acknowledge and express thanks for the valuable technical advice provided by J. Scott Cupples at NASA Johnson Space Center.
References Amick, R. Z., Reid, C. R., England, S. A., & Rajulu, S. L. (2015). Characterization of joint resistance and performance degradation of the extravehicular mobility unit spacesuit a pilot study. Proceedings of the Human Factors and Ergonomics Society e Annual Meeting, 59(1), 1259e1263. https://doi. org/10.1177/1541931215591202. Azouz, Z. B., Rioux, M., Shu, C., & Lepage, R. (2006). Characterizing human shape variation using 3D anthropometric data. The Visual Computer, 22(5), 302e314. https://doi.org/10.1007/s00371-006-0006-6. Blender Foundation. (2017). Blender e a 3D modelling and rendering package. https://www.blender.org/. Brown, J. (1975). Skylab 4 and ASTP crew height. National Aeronautics and Space Administration. Brown, J. (1977). Crew height measurement. In A. Nicogossian (Ed.), The apollo-soyuz test project medical report (pp. 119e121). Washington, DC: National Aeronautics and Space Administration. Churchill, E., Laubach, L. L., Mcconville, J. T., & Tebbetts, I. (1978). Anthropometric source book. In Anthropometry for designers (No. NASA-RP-1024, S-479-VOL-1) (Vol. 1). Houston, TX. Danckaers, F., Huysmans, T., Hallemans, A., De Bruyne, G., Truijen, S., & Sijbers, J. (2018). Full body statistical shape modeling with posture normalization. In D. N. Cassenti (Ed.), Advances in human factors in simulation and modeling (pp. 437e448). Springer International Publishing. England, S., Benson, E., Cowley, M., Harvill, L. R., Blackledge, C., Perez, E., & Rajulu, S. (2012). Comparative ergonomic evaluation of spacesuit and space vehicle design. In V. G. Duffy (Ed.), Advances in applied human modeling and simulation (1st ed., pp. 374e383). Boca Raton: CRC Press https://doi.org/10.1201/b12319. Gordon, C. C., Blackwell, C. L., Bradtmiller, B., Parham, J. L., Barrientos, P., Paquette, S. P., & Kristensen, S. (2014). 2012 anthropometric survey of U.S. Army personnel: Methods and summary statistics (No. NATICK/TR-15/007. Army Natick Soldier Research Development and Engineering Center.
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Kim, K. H., Young, K. S., Bernal, Y., Boppana, A., Vu, L. Q., Benson, E. A., & Rajulu, S. L. (2016). A parametric model of shoulder articulation for virtual assessment of space suit fit. In Presented at the 7th international conference on 3D body scanning technologies (pp. 201e207). Lugano, Switzerland. https://doi.org/10.15221/16.201 Margerum, S., Ferrer, M., Young, K., & Rajulu, S. (2010). Relating linear and volumetric variables through body scanning to improve human interfaces in space. In Proceedings of the first international conference on 3D body scanning technologies (pp. 010e022). Lugano, Switzerland. https://doi.org/10. 15221/10.010 NASA. (2015). EMU sizing: Requirements and constraints (FEMU-R-005) (No. Rev G (2015) JSC-65011/CTSD-SS-2970). Houston, TX: National Aeronautics and Space Administration. National Aeronautics and Space Administration. (1995). Man-system integration Standards (No. NASA-STD-3000 (1995, Rev B)). Houston, TX: National Aeronautics and Space Administration. National Aeronautics and Space Administration. (2000). In Flight and ground system specification (space Shuttle system payload accommodations No. NSTS 07700 (Vol. X). Houston, TX: National Aeronautics and Space Administration. Retrieved from https://standards.nasa.gov/standard/nasa/nasastd-3001-vol-1. National Aeronautics and Space Administration. (2015). NASA space flight human system Standards. In Human factors, habitability, and environmental health (No. NASA-STD-3001 (2015, Rev A)) (Vol. 2)Houston, TX: National Aeronautics and Space Administration. OpenCV. (2017). Open source computer vision library. Retrieved from http://opencv.org/. Ross, A., Rhodes, R., Graziosi, D., Jones, B., Lee, R., Haque, B. Z., & Gillespie, J. W. (2014). Z-2 prototype space suit development. In 44th international conference on environmental systems. Tucson, AZ. Thornton, W., Hoffler, G., & Rummel, J. (1977). Anthropometric changes and fluid shifts. In R. S. Johnston, & L. F. Dietlein (Eds.), Biomedical results from skylab (pp. 330e338). Washington, DC: National Aeronautics and Space Administration. Thornton, W., & Moore, T. (1987). Height changes in microgravity. In Results of the life sciences DSOs conducted aboard the space shuttle 1981e1986 (pp. 55e57). Houston, TX: Space Biomedical Research Institute, NASA. Williams, D. R., & Johnson, B. J. (2003). EMU shoulder injury tiger team report (No. NASA/TM-2003-212058, S-912). National Aeronautics and Space Administration. Wuhrer, S., & Shu, C. (2013). Estimating 3D human shapes from measurements. Machine Vision and Applications, 24(6), 1133e1147. https://doi.org/10. 1007/s00138-012-0472-y. Zhou, X., Sun, K., Roos, P. E., Li, P., & Corner, B. (2016). Anthropometry model generation based on ANSUR II database. International Journal of Dental Hygiene, 1(4), 321e343. https://doi.org/10.1504/IJDH.2016.084584.