A Framework To Guide The Assessment Of Human-Machine Systems

Keywords

Human-computer interaction; Human-machine interaction; Human-robot interaction; Performance; Safety

Abstract

Objective: We have developed a framework for guiding measurement in human-machine systems. Background: The assessment of safety and performance in human-machine systems often relies on direct measurement, such as tracking reaction time and accidents. However, safety and performance emerge from the combination of several variables. The assessment of precursors to safety and performance are thus an important part of predicting and improving outcomes in human-machine systems. Method: As part of an in-depth literature analysis involving peer-reviewed, empirical articles, we located and classified variables important to human-machine systems, giving a snapshot of the state of science on human-machine system safety and performance. Using this information, we created a framework of safety and performance in human-machine systems. Results: This framework details several inputs and processes that collectively influence safety and performance. Inputs are divided according to human, machine, and environmental inputs. Processes are divided into attitudes, behaviors, and cognitive variables. Each class of inputs influences the processes and, subsequently, outcomes that emerge in human-machine systems. Conclusion: This framework offers a useful starting point for understanding the current state of the science and measuring many of the complex variables relating to safety and performance in human-machine systems. Application: This framework can be applied to the design, development, and implementation of automated machines in spaceflight, military, and health care settings. We present a hypothetical example in our write-up of how it can be used to aid in project success.

Publication Date

3-1-2017

Publication Title

Human Factors

Volume

59

Issue

2

Number of Pages

172-188

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1177/0018720817695077

Socpus ID

85018174766 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85018174766

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