A Framework to Guide the Assessment of Human-Machine Systems

Area of Science:

• Human-machine systems engineering
• Systems safety and performance analysis

Background:

• Current safety and performance assessments in human-machine systems often rely on direct measurements.
• Safety and performance are emergent properties influenced by multiple interacting variables.
• Assessing precursors to safety and performance is crucial for predicting and enhancing outcomes.

Purpose of the Study:

• To develop a comprehensive framework for guiding measurement in human-machine systems.
• To synthesize the current scientific understanding of variables influencing human-machine system safety and performance.

Main Methods:

• Conducted an in-depth literature analysis of peer-reviewed, empirical articles.
• Located and classified variables critical to human-machine system safety and performance.
• Developed a framework based on the literature analysis.

Main Results:

• The framework categorizes inputs into human, machine, and environmental factors.
• Processes are classified into attitudinal, behavioral, and cognitive variables.
• The framework illustrates how inputs and processes collectively influence system outcomes.

Conclusions:

• The developed framework provides a foundational tool for understanding and measuring complex variables in human-machine systems.
• It offers a starting point for assessing the current state of scientific knowledge.
• The framework is applicable to various domains, including spaceflight, military, and healthcare.