Neil Armstrong’s digital twin: An integrative approach for movement analysis in simulated space missions
Benjamin Reimeir, Anna Wargel, Franziska Riedl, Sara Maach, Robert Weidner, Gernot Grömer, Peter Federolf
- 发表年份
- 2024
- 引用次数
- 3
- 访问权限
- 开放获取
摘要
Introduction and Purpose Space exploration is transitioning to a new era. NASA’s Artemis program is spearheading crewed missions to the Moon in the next decade, paving the way for eventual human exploration of Mars (Smith et al., 2020). However, space missions further afar from Earth require a shift in operational concepts towards an increasingly autonomous mission architecture due to communication delay (Belobrajdic et al., 2021). Analog space missions, such as AMADEE24, address these methodological challenges in a terrestrial simulation scenario (Preston & Dartnell, 2014). In order to imitate the biomechanical and perceptual constraints of suited operations in an analog mission, the astronauts wear space-suit simulators during extravehicular activities (EVA; Groemer et al., 2012). As EVAs account for the most physically-demanding and risky operations during space missions, the relevance for human movement science in space research is increasing. We propose an approach to integrate already available data sources to conduct comprehensive human movement analysis in planetary exploration scenarios. As a proof of concept, we aimed to identify sensitive kinematic and physiological markers for muscular fatigue in astronauts’ gait during the AMADEE24 mission conducted by the Austrian Space Forum. Methods The AMADEE24 Mars simulation took place from March 5th until April 5th 2024 in the Ararat province in Armenia, which was selected as simulation site for its geological similarities to areas on Mars. Six analog astronauts (two female, four male) lived isolated in a habitat for 21 days, conducting robotic, psychological and geoscientific experiments pertinent to future surface activities on Mars. Extensive high-resolution drone imaging of the analog region before the mission was conducted to compile a digital elevation model used for EVAs. Experiments in the field were conducted by two analog astronauts wearing the AOUDA space-suit simulator with two astronauts supporting them from the habitat using radio communication (Groemer et al., 2012). The recorded telemetry data of the suit included GPS location, heart rate, CO2- and O2-concentrations in the helmet as well as temperature and humidity measurements in the suit and primarily serves for medical monitoring of the astronauts during the EVA. Beneath the suit, the astronauts wore the inertial whole-body motion capture system XsensTM Awinda (Xsens Technologies B.V., Enschede, Netherlands) with 17 IMU-sensors and an 8-channel electromyography (EMG) system (Cometa myon, Barregio, Italy) for muscle activity recordings of the back, lower- and upper-extremities on the dominant side. For all experiments involving geoscientific operations by the analog astronauts, a remote-controlled robotic vehicle accompanied the astronauts in the field (Edlinger et al., 2022). The rover carried tools, samples and receiver for the motion-capture- and EMG-sensors. Additionally, the rover tracked the ambient conditions and took images of the surroundings, which are used to improve the 3D-environmental-model of the EVA region (see Figure 1). Biomechanical recordings were started remotely from the habitat. Prior to each EVA, astronauts prepared the experiment in the habitat following a controlled workflow. Four generic geoscientific operations common for geological sample-taking were performed at the beginning and end of the EVA by the test subject. The operations were performed at four different sites, each site approximately 40 to 60 m apart. A previously designed traverse plan shown in the head-up-display of the suit’s helmet defined the traverses for the astronauts. All four geoscientific operations as well as the ambulatory pathways were recorded by the motion capture- and EMG-systems. In a preliminary analysis of gait alterations, we investigated the pre- and post-EVA traverses to identify muscular fatigue accumulated over the 3 to 4 h-EVAs. The reprocessing of the motion capture recordings was perfor
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