Accelerated by commercial missions, human spaceflight becomes more common, while more crew time spent in orbit translates to more opportunities for using, testing and validating innovative solutions for health monitoring in space.
The adoption of AI/ML techniques in space can result in important steps forward for a wide range of human health research and applications, such as real-time Big Science Data processing, AI-powered assistants for medical care, research, evaluation and support of crew activities and performance, opening new portals into what future human spaceflight missions could look like.
During the RAKIA mission on Axiom-1, the ICE Cubes Service had the opportunity to support the implementation of several health apps solutions involving tablet applications used to perform a series of physiological and psychological tests on-board the ISS.
One activity from the RAKIA mission had the objective to conduct an ‘albumin to creatinine ratio’ (ACR) urinalysis test in space, with the aim to help astronauts measure kidney function and get immediate diagnostic results. This involved the use of a tablet app running a special (space edition) Kidney Test protocol. While the topic of kidney function in space has already been researched, previous studies required urine collected in space to be returned frozen back to Earth for post-flight analysis. This new app, instead, enabled astronaut crews to obtain immediate medical diagnostic information in-situ, which is particularly relevant as missions to space become longer.
Living in space can also affect eyesight and cause neuro-ocular syndrome (SANS). The RAKIA mission also included the utilisation of another tablet app that helped assess in real-time eye fitness before, during, and after a task, with the aim to investigate spaceflight effects on visual function, the early onset of spaceflight associated neuro-ocular syndrome, and the recovery of visual functions.
With increased human presence in space, and new space stations and habitats being developed to expand the scope of space missions, astronauts will need a variety of modern diagnostic tools to screen and monitor their health in the space environment. These tools and applications can be tested, validated and demonstrated on-board the ISS, and future space stations, in a fast-track and direct manner through the ICE Cubes Service, which also provides AI/ML capabilities and support for big data processing and analysis.
In modern scientific research, AI is already playing a crucial role in providing in-situ, fast, real-time processing of big data generated by a variety of experiments, measurements and activities, either in crewed or fully-automated spacecraft. One can therefore predict that the application of AI/ML assets will support scientists and operators in more efficiently understanding and monitoring the effects of the space environment and microgravity in all its facets, by deciphering models and patterns that are not easily observable. This opens new avenues for R&D in life sciences, biotech & pharma, agrifoodtech, and many applications relevant to the realm of human health monitoring and protection, providing a boost in computational capability for data analysis, problem solving or automation of complex tasks.
The effects of long-term exposure to spaceflight conditions manifest in many areas of (human) physiology: neurology, ophthalmology, cardiovascular, pulmonary, gastrointestinal, urinary, musculoskeletal, hematology, immunology, oncology, and psychological stress (N. M. Haney et al, 2020)
AI/ML techniques can enhance in-space research in any of these areas, at different scales and under different setups, by combining and analyzing data stemming from specific individual processes of sub-systems (e.g. organoids, spheroids, tissues, genomics via lab/organ-on-chip research, etc.), or from larger systems and their processes (e.g. astronauts as health research subjects).
Commercial spaceflight comes with a different approach to conducting activities in space, tapping into the solutions already available on different non-space/terrestrial markets. In recent years, the rapid development of technology on Earth offers a wide range of tools, applications and off-the-shelf solutions that can be quickly adapted for utilization in space. This not only allows for more advanced activities and processes, but also significantly shortens the time required for integration, implementation of R&D activities, data retrieval and analysis. Such solutions include wearable devices, biosensors, diagnostic imaging, augmented and virtual reality tools.
The empowerment of such applications with AI/ML capabilities can pave the way for the implementation of innovative health monitoring and evaluation solutions. The validation and demonstration of these solutions in space can then become part of the health check protocols and evaluation process of space crews, enabling for an automatic data analysis in orbit, while also providing real-time connection for data and results comparison and cross-checking by the medical staff on ground.
Combining research payloads and devices with edge computing capabilities in orbit offers the opportunity to test and benchmark new solutions and applications required by future long-term deep space exploration missions, where (near) real-time communication with ground (Earth) is not possible. Such solutions may include machine vision applications, in-situ processing and analysis of data via AI/ML powered visual inspection, development of smart devices, robotic assistants and habitats, as well as AR/VR-based applications for future space activities and operations (in-orbit and on-ground).
In July we participated to the 42nd Annual International Society for Gravitational Physiology (ISGP) meeting in Antwerp, Belgium, where we talked about “New commercial applications for space physiology and human spaceflight”.
The presentation slides can be found here.