Prediction of physical workload in reduced gravity.

Area of Science:

• Space Exploration
• Human Physiology
• Biomechanical Engineering

Background:

• Long-term space missions require accurate prediction of mission support needs, including food and water.
• Estimating human energy expenditure is vital for resource allocation, personnel assignment, and work-rest schedules in low-gravity environments.
• Existing models for energy expenditure in industrial settings need adaptation for reduced gravity.

Purpose of the Study:

• To adapt and validate an elemental analysis approach for predicting human energy expenditure in low-gravity conditions.
• To assess the model's accuracy against historical space mission data.

Main Methods:

• Adapted a well-established industrial energy expenditure model by reducing body and load weights proportionally to decreased gravity.
• Validated the adapted model using Apollo-era data, including simulated lunar gravity walking, upper body torquing, and cart pulling experiments.
• Compared model predictions with observed energy expenditure values from various simulated and actual lunar gravity tasks.

Main Results:

• The adapted energy expenditure model generally underpredicted high energy expenditures.
• The model overpredicted low to medium energy expenditures but remained within 15-30% of actual values for these workloads.
• The model demonstrated feasibility but requires further development for enhanced accuracy.

Conclusions:

• The elemental analysis approach, with gravity-adjusted weights, offers a foundational method for estimating energy expenditure in low-gravity environments.
• Further research is needed to incorporate factors like traction changes and other nonlinearities specific to reduced gravity.
• Refined models will improve the accuracy of mission support requirement predictions for sustained space habitation.