CC20 NASA Spacesuit Detection Challenge

A spacesuit has unique movement patterns that can be observed in Extravehicular Activities (EVA), and mobility assessments are needed to discern and mitigate suit injury risk. However, current motion capture systems are cost prohibitive and not feasible for some training conditions (i.e. simulated lunar outdoor terrain). Thus, there is a need to quantify suit motion from conventional photographs or video without using any specialized sensors as video information is prevalent and easily collected. Understanding how a person moves in the spacesuit enables the characterization of EVA motions, which can be used to optimize EVA tasks, training, and hardware. What is the current performance of the system and why/how would improvements yield a significant impact? : Preliminary work has been done to estimate suit posture from photographs using an iterative parameter optimization algorithm. However, a broader and more complex machine learning approach is needed to account for variability in viewing angles and environments. The developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize the suit, hardware and task designs. For example, biomechanical stress assessments for EVA can be performed to mitigate injury risk. The framework is applicable to Neutral Buoyancy Lab (NBL) and Active Response Gravity Offload System (ARGOS) testing, which will help to optimize training procedures. The tool can be further tested against a large wealth of video archives of past EVAs for task and timeline analysis.
A spacesuit has unique movement patterns that can be observed during spacewalks or Extravehicular Activities (EVA), and mobility assessments are needed to discern and mitigate suit injury risk. It is very difficult to measure spacesuit motion in uncontrolled environments such as training facilities, and a novel method is needed to quantify spacesuit motions from conventional and readily available video and photographs without requiring or needing motion capture cameras. Once validated for the accuracy and reliability of the posture extractions, the selected system will be deployed to estimate the EVA postures in current and future missions and analog training events. The framework will be applicable to Neutral Buoyancy Lab (NBL) and Active Response Gravity Offload System (ARGOS) testing, which will help to optimize training procedures. Additionally, the winning solution will be tested and validated on video recordings collected during the next-generation spacesuit testing. NASA is seeking novel solutions to label and identify spacesuit motions from conventional and readily available video and photographs to overcome current system limitations in terms of cost and training feasibility. The winning computer vision algorithms are expected to have the ability to: Detect spacesuits in a variety of environments and lightning conditions. Correctly discriminate between an "unsuited" person and a spacesuit. Robustly extract suit postures from images partially occluded. Capable of functioning with a single or multiple spacesuits. A spacesuit has unique movement patterns that can be observed in Extravehicular Activities (EVA), and mobility assessments are needed to discern and mitigate suit injury risk. However, current motion capture systems are cost prohibitive and not feasible for some training conditions (i.e. simulated lunar outdoor terrain). Thus, there is a need to quantify suit motion from conventional photographs or video without using any specialized sensors as video information is prevalent and easily collected. Understanding how a person moves in the spacesuit enables the characterization of EVA motions, which can be used to optimize EVA tasks, training, and hardware. What is the current performance of the system and why/how would improvements yield a significant impact? : Preliminary work has been done to estimate suit posture from photographs using an iterative parameter optimization algorithm. However, a broader and more complex machine learning approach is needed to account for variability in viewing angles and environments. The developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize the suit, hardware and task designs. For example, biomechanical stress assessments for EVA can be performed to mitigate injury risk. The framework is applicable to Neutral Buoyancy Lab (NBL) and Active Response Gravity Offload System (ARGOS) testing, which will help to optimize training procedures. The tool can be further tested against a large wealth of video archives of past EVAs for task and timeline analysis.

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