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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 Earth-like environments. These systems must support human mobility and physical workload demands while minimizing injury risk during extravehicular activity (EVA). Future EVA operations on the Lunar surface are expected to be more frequent and require higher physical workloads than previously during the ISS, Shuttle, and Apollo programs. To characterize the workloads and ergonomics needs a suit must support, the kinematics of the space suit must be measured during operationally-relevant tasks in ground analog environments. Kinematics capture of the suit is challenging for traditional optical motion capture (OMC) approaches due to marker occlusion, harsh lighting or environmental conditions, and tests with suit surrogates in outdoor field environments. To this end, engineers at NASA are developing the Augmented Suit Inverse Kinematics (ASIK) system, a complete motion capture method and inverse kinematics solver which relies solely on a network of wireless inertial measurement units (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 poses. 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 Johnson Space Center in Houston, TX. The suits were outfitted with 12 IMUs to estimate lower body and trunk kinematics. The suits were also outfitted with a set of reflective OMC markers, and traditional OMC data was collected and processed. Characterization of the ASIK-derived suit joint angles’ accuracy against an optical motion capture datum will be presented. Discussion of these results, as well as discussion of system calibration and nuances of mathematical observability, will be included.

IMU↗
NASA NTRS2016

Utilizing Commercial Hardware and Open Source Computer Vision Software to Perform Motion Capture for Reduced Gravity Flight

Long duration space travel to Mars or to an asteroid will expose astronauts to extended periods of reduced gravity. Since gravity is not present to aid loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to minimize the loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Unlike the International Space Station (ISS), the area available for an exercise device in the next generation of spacecraft is limited. Therefore, compact resistance exercise device prototypes are being developed. The NASA Digital Astronaut Project (DAP) is supporting the Advanced Exercise Concepts (AEC) Project, Exercise Physiology and Countermeasures (ExPC) project and the National Space Biomedical Research Institute (NSBRI) funded researchers by developing computational models of exercising with these new advanced exercise device concepts. To perform validation of these models and to support the Advanced Exercise Concepts Project, several candidate devices have been flown onboard NASAs Reduced Gravity Aircraft. In terrestrial laboratories, researchers typically have available to them motion capture systems for the measurement of subject kinematics. Onboard the parabolic flight aircraft it is not practical to utilize the traditional motion capture systems due to the large working volume they require and their relatively high replacement cost if damaged. To support measuring kinematics on board parabolic aircraft, a motion capture system is being developed utilizing open source computer vision code with commercial off the shelf (COTS) video camera hardware. While the systems accuracy is lower than lab setups, it provides a means to produce quantitative comparison motion capture kinematic data. Additionally, data such as required exercise volume for small spaces such as the Orion capsule can be determined. METHODS: OpenCV is an open source computer vision library that provides the ability to perform multi-camera 3 dimensional reconstruction. Utilizing OpenCV, via the Python programming language, a set of tools has been developed to perform motion capture in confined spaces using commercial cameras. Four Sony Video Cameras were intrinsically calibrated prior to flight. Intrinsic calibration provides a set of camera specific parameters to remove geometric distortion of the lens and sensor (specific to each individual camera). A set of high contrast markers were placed on the exercising subject (safety also necessitated that they be soft in case they become detached during parabolic flight); small yarn balls were used. Extrinsic calibration, the determination of camera location and orientation parameters, is performed using fixed landmark markers shared by the camera scenes. Additionally a wand calibration, the sweeping of the camera scenes simultaneously, was also performed. Techniques have been developed to perform intrinsic calibration, extrinsic calibration, isolation of the markers in the scene, calculation of marker 2D centroids, and 3D reconstruction from multiple cameras. These methods have been tested in the laboratory side-by-side comparison to a traditional motion capture system and also on a parabolic flight.

Biodynamics↗
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↗
NASA NTRSDate not supplied

Evaluation of Markerless Motion Capture for Monitoring Sensorimotor Performance

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

Hannah M. Weiss↗
NASA NTRSDate not supplied

Accuracy of Center of Pressure Determination via Motion Capture

BACKGROUND: This study was conducted to support the stability assessment for tasks in lunar gravity and exercises on a Vibration Isolation and Stabilization (VIS) system in microgravity based on the dynamic feasibility criterion of whether the calculated position of the center of pressure (COP) falls within the base of support (BOS) which outlines the subject’s feet. Motion capture data combined with biomechanical modeling and simulation allows the forces and moments between the human and the VIS platform to be computed and the position of the COP as well as the location and shape of the BOS to be determined. The goal of this study was to assess the accuracy of the COP trajectory calculated using motion capture-based data. METHODS AND RESULTS: To obtain the dynamic quantities from which COP is calculated, motion capture data is first collected in the 1g lab environment by recording the trajectories of passive retroreflective markers placed on a subject during exercise or performance of a given task. The OpenSim [1] inverse kinematics (IK) tool is used to fit a scaled subject model to recorded marker trajectories while minimizing marker error to obtain joint angles. Then, a custom OpenSim plugin [2] is used to determine the subject’s time-varying moment of inertia and its time derivative, center of mass (CM) position, velocity, and acceleration, as well as the angular momentum and its time derivative relative to the subject’s CM. Some of these quantities are not needed for modeling tasks performed on a stationary lunar surface but, due to the moving exercise platform, are needed to model VIS response to the subject’s motion. Hand positions, used in calculating a cable force if present, are recorded as well. These quantities are used to calculate the total force (F ⃗^((plate) )) and moment (M ⃗^((plate) )) exerted by the lunar surface or the VIS plate on the subject’s shoe soles. COP is then calculated from the following equations: r_x^((cop) )= M_z^((plate) )/F_y^((plate) ) and r_z^((cop) )= 〖-M〗_x^((plate) )/F_y^((plate) ), where the y axis is normal to the surface. COP accuracy for feasibility assessments is then determined by whether it falls within the BOS, which is also computed by the plugin. To study the accuracy of COP calculated from motion capture, we first investigated whether COP remained within the BOS, as it must, for exercises performed in the 1g lab environment. Standard exercises such as back squat and deadlift were analyzed, as well as more explosive exercises including hang clean and press. Cases in which the COP exited the BOS indicated that COP accuracy required further investigation. In this study, an exercise device with cables was used, so cable force modeling accuracy should also be considered. In a separate study, we collected motion capture and force plate data for twenty-seven motions not involving an exercise device. About a third were genuine countermeasures exercises (e.g., hang clean and press), some were relevant for lunar tasks (e.g., object pick up), and the rest were of a “unit test” nature (e.g., swaying back and forth or side to side). Motion capture-based COP positions were compared with force plate measured COP. We found that while force plate measured COP remained within the BOS, motion capture-based COP was observed to briefly exit the BOS on occasion. Techniques to mitigate IK artifacts and filtering of calculated data could be used to improve the agreement of calculated and measured results, resolving excursions from the BOS within this dataset. The mean error between calculated and measured COP was found to be less than 6 mm. Additionally, we derived and investigated equations for the COP in terms of the cable force, cable location, as well as the human CM position, acceleration, and angular momentum with respect to the CM, and analyzed them for sensitivity to errors in individual quantities. Several were found, but the most significant one was that when the vertical force on the feet approaches zero, indicating a near-detachment or ‘jump off’ condition, errors are amplified. This is consistent with the observation that in the absence of pressure, the concept of the center of pressure would become meaningless.

C A Bell↗
NASA NTRS2015

Exercise Sensing and Pose Recovery Inference Tool (ESPRIT) - A Compact Stereo-based Motion Capture Solution For Exercise Monitoring

Crew exercise is important during long-duration space flight not only for maintaining health and fitness but also for preventing adverse health problems, such as losses in muscle strength and bone density. Monitoring crew exercise via motion capture and kinematic analysis aids understanding of the effects of microgravity on exercise and helps ensure that exercise prescriptions are effective. Intelligent Automation, Inc., has developed ESPRIT to monitor exercise activities, detect body markers, extract image features, and recover three-dimensional (3D) kinematic body poses. The system relies on prior knowledge and modeling of the human body and on advanced statistical inference techniques to achieve robust and accurate motion capture. In Phase I, the company demonstrated motion capture of several exercises, including walking, curling, and dead lifting. Phase II efforts focused on enhancing algorithms and delivering an ESPRIT prototype for testing and demonstration.

Lee, Mun Wai↗
NASA NTRS2001

A Quasi-Static Method for Determining the Characteristics of a Motion Capture Camera System in a "Split-Volume" Configuration

To confidently report any data collected from a video-based motion capture system, its functional characteristics must be determined, namely accuracy, repeatability and resolution. Many researchers have examined these characteristics with motion capture systems, but they used only two cameras, positioned 90 degrees to each other. Everaert used 4 cameras, but all were aligned along major axes (two in x, one in y and z). Richards compared the characteristics of different commercially available systems set-up in practical configurations, but all cameras viewed a single calibration volume. The purpose of this study was to determine the accuracy, repeatability and resolution of a 6-camera Motion Analysis system in a split-volume configuration using a quasistatic methodology.

Miller, Chris↗
NASA NTRS2019

Using Virtual Reality and Motion Capture as tools for Human Factors Engineering at NASA Marshall Space Flight Center

NASA Marshall Space Flight Center (MSFC) Human Factors Engineering (HFE) Team is implementing virtual reality (VR) and motion capture (MoCap) into HFE analyses of various projects through its Virtual Environments Lab (VEL). VR allows for multiple analyses early in the design process and more opportunities to give design feedback. This tool can be used by engineers in most disciplines to compare design alternatives and is particularly valuable to HFE to give early input during these evaluations. These techniques are being implemented for concept development of Deep Space Habitats (DSH), and work is being done to implement VR for design aspects of the Space Launch System (SLS). VR utilization in the VEL will push the design to be better formulated before mockups are constructed, saving budget and time. The MSFC VEL will continue forward leaning implementation with VR technologies in these and other projects for better models earlier in the design process.

Andrews, Tanya↗
NASA NTRS2019

Using Virtual Reality and Motion Capture as tools for Human Factors Engineering at NASA Marshall Space Flight Center

NASA Marshall Space Flight Center (MSFC) Human Factors Engineering (HFE) Team is implementing virtual reality (VR) and motion capture (MoCap) into HFE analyses of various projects through its Virtual Environments Lab (VEL). These techniques are being implemented for concept of development of Deep Space Habitats (DSH) and design and analyses for NASA’s Space Launch System (SLS). VR utilization in the VEL will push the design to be better formulated before mockups are constructed, saving budget and time.

Andrews, Tanya↗
NASA NTRS2012

Motion-Capture-Enabled Software for Gestural Control of 3D Models

Current state-of-the-art systems use general-purpose input devices such as a keyboard, mouse, or joystick that map to tasks in unintuitive ways. This software enables a person to control intuitively the position, size, and orientation of synthetic objects in a 3D virtual environment. It makes possible the simultaneous control of the 3D position, scale, and orientation of 3D objects using natural gestures. Enabling the control of 3D objects using a commercial motion-capture system allows for natural mapping of the many degrees of freedom of the human body to the manipulation of the 3D objects. It reduces training time for this kind of task, and eliminates the need to create an expensive, special-purpose controller.

Norris, Jeffrey S.↗
NASA NTRSDate not supplied

Volumetric Assessment of UPRITE Exercises From Marker-Based Motion Capture

Lack of volumetric data on full-body movement exercises presents a challenge to ensuring the fit of crew members’ full range of motion on the International Space Station (ISS). The Upright Proprioception Retention via In-flight Training and Evaluation (UPRITE) is a sensorimotor countermeasure device designed for maintaining crew members’ proprioception in a microgravity environment (Figure 1). This work compares differences between data collection-based “high-fidelity” volumes and a “cone volume” with simplified assumptions (95th percentile in stature human standing on UPRITE while tilted and revolved around vertical axis). Objective: To provide the spatial parameters of exercises on the UPRITE such that it is placed on the ISS according to its volumetric demands or that usage is adjusted to fit the available space.

L D Quinto↗
NASA NTRSDate not supplied

Volumetric Assessment of UPRITE Exercises From Marker-Based Motion Capture

BACKGROUND Lack of volumetric data on full-body movement of exercises presents a challenge to ensuring the fit of crew member’s full range of motion on the International Space Station (ISS). The Upright Proprioception Retention via In-flight Training and Evaluation (UPRITE) is a sensorimotor countermeasure device designed for maintaining crew members’ proprioception in a microgravity environment. A footplate—attached to a static base—rotates in two degrees of freedom (pitch and roll) up to a 20 deg angle. An initial volumetric assessment assuming an upright standing posture produced a cone-like shape with a narrow bottom and wide top. Such general volumetric assessments risk creating an overly conservative volume estimate, taking up more space than is necessary on the already limited interior space of the ISS, and neglecting necessary volume due to oversimplifying assumptions. Rather, higher-fidelity volumetric assessments offer more comprehensive insights in an environment where every area counts. The main objective of this work is to provide the spatial parameters of exercises on the UPRITE such that it is placed on the ISS according to its volumetric demands or that usage is adjusted to fit the available space. METHODS In 2023, a data collection was performed originally to inform loads and dynamics of system use and was recently leveraged for volumetric assessment. Three human subjects representing different body types (~63-76 inches in stature) performed a variety of board manipulations using UPRITE with body weight offload. The test collected the 3D positional data of a modified full-body Plug-in Gait marker set [1] via a 16-camera OptiTrack MoCap system. After processing – filling marker gaps and trimming data – in OptiTrack Motive, the recorded marker location data, which included device markers, was exported to a readable trajectory file. To accurately represent the full volume defining landmarks, additional markers were digitally added to an unscaled Modified Full Body Model [2]. The model was then scaled according to its subject parameters upon which an inverse kinematics analysis was performed. A custom plugin yielded model marker location data files. Volumetric analyses were performed on the recorded trajectory and model trajectory files using a custom Python-built tool that extracted the marker location data and plotted it in a 3D space. Concerned with only the maximum volume of the motion, a 3D convex hull analysis was applied to the plot, extracting the vertices or external points of the eventual 3D CAD output, dubbed aptly as a “volume shell”. This overall approach was based on guidance in a NASA-STD-3001 Technical Brief [3]. RESULTS AND DISCUSSION Batch volumetric assessment on the exercises for each subject was performed, producing high-fidelity volume shells in minimal time. Preliminary results highlighted the value in higher-fidelity volumes based on collected data when possible. For example, revolving a single posture in the cone assessment would not have sufficiently captured a single leg stance; rather, it would need to involve swinging the leg both forward and back. Additional observations and the maximal dimensions of the volumes, including those based on scaled data for ISS anthropometric requirements, will be presented at the Human Research Program Investigator’s Workshop. CONCLUSIONS While this work’s primary objective was for the UPRITE-to-ISS integration, the tool built to conduct this analysis has wide applications for future exercise systems as an informational tool for optimal device placement. The tool and its findings also have implications for exercise device design and spacecraft interior considerations on Gateway, the Lunar Pressurized Rover, and beyond. REFERENCES [1] Bell, C. A., et al. (2023) Recent Improvements and Verification of a Full Body Model in OpenSim. NASA Human Research Program Investigator’s Workshop. https://ntrs.nasa.gov/citations/20230001080 [2] Lostroscio, K., et al (2023) The Digital Astronaut Simulation. AHFE International Conference on Human Factors in Design, Engineering, and Computing for All. [3] Exercise Overview. (2023) NASA-STD-3001 Technical Brief. https://www.nasa.gov/wp-content/uploads/2023/12/ochmo-tb-031-exercise-overview.pdf?emrc=9d454c?emrc=9d454c

L D Quinto↗
NASA NTRS2002

A quasi-static method for determining the characteristics of a motion capture camera system in a "split-volume" configuration

The purpose of this study was to determine the accuracy, repeatability and resolution of a six-camera Motion Analysis system in a vertical split-volume configuration using a unique quasi-static methodology. The position of a reflective marker was recorded while it was moved quasi-statically over a range of 2.54 mm (0.100 inches) via a linearly-translating table. The table was placed at five different heights to cover both sub-volumes and the overlapping region. Data analysis showed that accuracy, repeatability and resolution values were consistent across all regions of the split-volume, including the overlapping section.

NASA Center JSC↗
NASA NTRS2019

Using Virtual Reality and Motion Capture as Tools for Human Factors Engineering at Nasa Marshall Space Flight Center

MSFC’s Human Factors Engineering (HFE) team is responsible for all worksite analyses performed for the SLS pre-launch integration activities at Kennedy Space Center (KSC). There is a wide variety of tasks associated with pre-launch integration activities and it is important to verify that vehicle integration will be successful early in the design process. The VR work performed by the HFE team at MSFC has allowed fast changing layouts to be analyzed by various departments with minimal impact to cost or schedule. Implementing these methods for SLS allows for VR use in early design cycles, saving time and budget. Utilizing the resulting HFE analyses improves usability and safety. Ultimately, the goal is to provide a safe environment for the technicians assembling the vehicle and the astronaut crew at launch.

Andrews, Tanya↗
NASA NTRS2022

Accuracy of Center of Pressure Determination Via Motion Capture

This study was conducted to support stability assessment for: (1) Tasks in lunar gravity (2) Exercises on a Vibration Isolation and Stabilization (VIS) system in microgravity. Stability assessment based on the dynamic feasibility criterion of whether the calculated position of the center of pressure (COP) falls within the base of support (BOS) which outlines the subject’s feet.

C A Bell↗