Human performance measures for the evaluation of process control human-system interfaces in high-fidelity simulations

Jie XU*, Shilo ANDERS, Arisa PRUTTIANAN, Daniel FRANCE, Nathan LAU, Julie A. ADAMS, Matthew B. WEINGER

*Corresponding author for this work

Research output: Journal PublicationsReview articleOther Review

16 Citations (Scopus)

Abstract

We reviewed the available literature on measuring human performance to evaluate human-system interfaces (HSIs), focused on high-fidelity simulations of industrial process control systems, to identify best practices and future directions for research and operations. We searched the available literature and then conducted in-depth review, structured coding, and analysis of 49 articles, which described 42 studies. Human performance measures were classified across six dimensions: task performance, workload, situation awareness, teamwork/collaboration, plant performance, and other cognitive performance indicators. Many studies measured performance in more than one dimension, but few studies addressed more than three dimensions. Only a few measures demonstrated acceptable levels of reliability, validity, and sensitivity in the reviewed studies in this research domain. More research is required to assess the measurement qualities of the commonly used measures. The results can provide guidance to direct future research and practice for human performance measurement in process control HSI design and deployment.
Original languageEnglish
Pages (from-to)151-165
Number of pages15
JournalApplied Ergonomics
Volume73
DOIs
Publication statusPublished - Nov 2018
Externally publishedYes

Bibliographical note

The authors acknowledge the support and constructive comments of Katya Le Blanc and Bruce Hallbert at the Idaho National Laboratory.

Keywords

  • Human performance measure
  • Human-system interface
  • Process control
  • Systematic review

Fingerprint

Dive into the research topics of 'Human performance measures for the evaluation of process control human-system interfaces in high-fidelity simulations'. Together they form a unique fingerprint.

Cite this