Monitoring and modulating interconnected physiological systems in space using portable closed-loop technologies

Area of Science:

• Space Medicine
• Network Physiology
• Artificial Intelligence in Health

Background:

• Human spaceflight involves numerous stressors (microgravity, radiation, isolation) affecting biological, cognitive, and psychological health.
• These stressors can lead to deconditioning, sleep disruption, impaired cognition, and mood disturbances.
• Longer missions necessitate advanced, portable technologies for continuous health monitoring and real-time interventions.

Purpose of the Study:

• To propose a network physiology framework for understanding spaceflight's multifactorial health consequences.
• To explore the use of advanced sensing and AI for continuous monitoring and personalized countermeasures.
• To discuss AI-guided interventions for maintaining astronaut health and performance.

Main Methods:

• Utilizing a network physiology perspective to view physiological functions as interconnected systems.
• Leveraging multimodal wearable and habitat-integrated sensors to capture neural, autonomic, cardiovascular, thermal, and behavioral signals.
• Applying AI-based analysis to longitudinal data for identifying deviations and deriving resilience markers.

Main Results:

• The network physiology framework offers a comprehensive view of how spaceflight stressors propagate through interconnected systems.
• Multimodal sensing and AI enable continuous, system-level monitoring of astronaut adaptation.
• AI analysis can identify deviations from adaptive states and guide personalized interventions.

Conclusions:

• A network physiology approach, supported by AI and advanced sensing, is crucial for managing astronaut health during long-duration space missions.
• This framework can help identify multisystem dysfunction and guide personalized, non-pharmacological interventions.
• The approach has potential applications for precision health on Earth.