Preventive measures and countermeasure exercises play integral roles in maintaining the physiological health, wellbeing, and performance of astronauts in current day four to 11 month space missions to the ISS. While these activities help the body adapt to the adverse effects of spaceflight and minimize risks of physical deconditioning associated with weightlessness, these activities are inadequate for longer trips such as a 2–3 year mission to and from Mars. Furthermore, physical reconditioning and other issues such as visual impairment intracranial pressure (VIIP) syndrome remain for some astronauts returning from space missions. Combined with the effects of isolation and confinement in space, an astronaut's condition and performance can be compromised to a high degree with long-term impacts to their health and wellness upon returning to Earth if the appropriate interventions are not performed at appropriate times. Research has shown that representation of the relevant data to the active user during their activities has been proven effective in allowing them to perform the appropriate intervention to mitigate projected health risks. The physiological data and countermeasure equipment data currently located on the ISS has the potential to be correlated with the respective activities performed per astronaut for an individualized physiological monitoring approach for real-time health assessments. This paper presents a correlation method to enable individualized countermeasure assessments using big data collected during a simulated extreme environment workshop for firefighters as an analog. The online health analytics platform, created by McGregor, known as Artemis demonstrates this method using its capabilities in temporal abstraction for knowledge discovery, mechanisms for early detection of illnesses, and continuous real-time monitoring.


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    Titel :

    An Individualized Countermeasure Assessment Framework for Astronauts in Space


    Beteiligte:


    Erscheinungsdatum :

    2021-03-06


    Format / Umfang :

    4016024 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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