Psychological Monitoring in Interstellar Travel: Application of Machine Learning in Space Isolation Environments

This project focuses on the assessment of crew members' psychological states in interstellar travel isolation environments. By using machine learning to analyze video data, it develops a non-invasive monitoring system, laying the foundation for future automated health monitoring while also having wide-ranging ground application value.

Interstellar travel faces psychological challenges such as longer isolation periods and a sense of separation from Earth, which may lead to sleep disorders, mood swings, and other issues. Ground simulation experiments like Mars 500 have revealed these risks, so establishing an effective psychological monitoring mechanism is crucial for mission safety.

The project aims to develop a machine learning-driven psychological assessment system. Using video analysis technology, it non-invasively captures changes in crew members' psychological states without the need for additional physiological sensors, reducing interference with crew members and laying the foundation for automated health monitoring systems.

1. Data collection and preprocessing: Cameras record activities; preprocessing includes face detection, key point extraction, etc., converting to structured features.
2. Feature engineering: Extract multi-dimensional features such as facial (expression, eye gaze), posture (gestures, energy), behavior (social distance), and temporal (periodic changes).
3. Clustering analysis: Use unsupervised algorithms (e.g., K-means) to automatically discover psychological state clusters (e.g., active social interaction, anxiety and restlessness).

• Real-time health monitoring: Continuously monitor crew status and warn of risks in time.
• Ground applications: Polar research stations, submarine missions, ICU patient assessment, care for the elderly living alone, etc.
• Scientific value: Provide objective and continuous psychological data to compensate for the strong subjectivity of traditional questionnaires.

• Privacy protection: Adopt edge computing; delete original videos after local feature extraction and transmit encrypted features.
• Individual differences: Use personalized modeling to identify anomalies from individual baselines.
• Environmental interference: Enhance model robustness to distinguish behavior changes caused by environmental factors vs. psychological factors.

In the future, we will explore directions such as multimodal fusion (video + audio + physiological signals), causal inference, automatic intervention suggestion generation, and long-term mental health trend prediction.

This project demonstrates the potential of machine learning in mental health monitoring in extreme environments. It will become a tool for space mission support and also provide new ideas for ground mental health care. We look forward to more cross-innovations between AI and aerospace medicine.