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K. Han Kim

Publications and source records attributed to K. Han Kim.

7 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

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

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

Linh Vu↗
NASA NTRSDate not supplied

Development of ARGOS Offloading Assessments and Methodology for Lunar EVA Simulations

The Active Response Gravity Offload System (ARGOS) at NASA Johnson Space Center (JSC) is an analog environment that can offload pressurized suited subjects at various gravity levels. The suit is suspended from a robotic overhead crane by a cable connected to the suit via a gimbal with an adjustable pivot point. There has been increased interest in providing lunar pressurized suited training at ARGOS in preparation for lunar missions. Determination of the appropriate gimbal pivot point location for a given subject is vital for a high-fidelity and functional lunar simulation. Interactions between the pivot point location and center of gravity (CG) can result in righting moments that may lead to artificially stable or unrealistically challenging configurations. Changing the pivot point location is time consuming and repeated adjustment can result in significant loss of valuable pressurized suited time. This paper aims to share the knowledge obtained from the offloading characterization efforts during pressurized suited testing at ARGOS and document the ongoing process to define an appropriate pivot point location through iterative quantitative and qualitative assessments. The human-spacesuit CG locations for the ARGOS lunar simulation were estimated using a 3D body scan and density model combined with spacesuit hardware CAD and specifications. Early pilot testing of the gimbal revealed that setting the pivot point coincident with the modeled CG location was not always possible due to the current gimbal design, and small shifts forward and aft had noticeable effects on subject stability. Fifteen subjects performed a series of CG-related tasks in the xEMU spacesuit to assess simulation acceptability. Through iterative testing, this task list evolved to streamline the process needed to efficiently identify a suitable pivot point for a given subject. The developed methodology will be critical to determine pivot point selection for astronaut training in the xEMU ARGOS environment.

Sarah L. Jarvis↗
NASA NTRSDate not supplied

Development of ARGOS (Active Response Gravity Offload System) Offloading Assessments and Methodology for Lunar EVA Simulations

The Active Response Gravity Offload System (ARGOS) at NASA Johnson Space Center (JSC) is an analog environment that can offload pressurized suited subjects for various reduced gravity simulations. The suit is suspended from a robotic overhead crane by a cable connected to the suit via a gimbal with an adjustable pivot point (i.e. offload attachment). There has been increased interest in providing planetary pressurized suited training at ARGOS in preparation for lunar missions. Determination of the appropriate gimbal pivot point location for a given subject is vital for a high-fidelity functional lunar simulation. Interactions between the pivot point location and human-spacesuit center of gravity (CG) can result in righting moments that may lead to artificially stable or unrealistically challenging configurations. Changing the pivot point location is time consuming and repeated adjustment can result in loss of valuable pressurized suited time. This paper aims to share knowledge obtained from the offloading characterization efforts during pressurized suited testing at ARGOS and document the ongoing process to define an appropriate pivot point location through iterative quantitative and qualitative assessments. Human-spacesuit CG locations for the ARGOS lunar simulation were estimated using a 3D body scan and density model combined with spacesuit hardware CAD and specifications. Early pilot testing of the gimbal revealed that setting the pivot point coincident with the modeled CG location was not always possible due to the current gimbal design, and small pivot point shifts had noticeable effects on subject stability. Fourteen subjects performed a series of CG-related tasks in the Exploration Extravehicular Mobility Unit (xEMU) to assess simulation characteristics. Through iterative testing, this task list evolved to streamline the process needed to efficiently identify a suitable pivot point for a given subject. The developed methodology will be critical for pivot point selection during astronaut training in the ARGOS environment.

Sarah L. Jarvis↗