Literature DB >> 15960084

Designing effective human-automation-plant interfaces: a control-theoretic perspective.

Greg A Jamieson1, Kim J Vicente.   

Abstract

In this article, we propose the application of a control-theoretic framework to human-automation interaction. The framework consists of a set of conceptual distinctions that should be respected in automation research and design. We demonstrate how existing automation interface designs in some nuclear plants fail to recognize these distinctions. We further show the value of the approach by applying it to modes of automation. The design guidelines that have been proposed in the automation literature are evaluated from the perspective of the framework. This comparison shows that the framework reveals insights that are frequently overlooked in this literature. A new set of design guidelines is introduced that builds upon the contributions of previous research and draws complementary insights from the control-theoretic framework. The result is a coherent and systematic approach to the design of human-automation-plant interfaces that will yield more concrete design criteria and a broader set of design tools. Applications of this research include improving the effectiveness of human-automation interaction design and the relevance of human-automation interaction research.

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Year:  2005        PMID: 15960084     DOI: 10.1518/0018720053653820

Source DB:  PubMed          Journal:  Hum Factors        ISSN: 0018-7208            Impact factor:   2.888


  3 in total

1.  Human-Automation Interaction Design for Adaptive Cruise Control Systems of Ground Vehicles.

Authors:  Hwisoo Eom; Sang Hun Lee
Journal:  Sensors (Basel)       Date:  2015-06-12       Impact factor: 3.576

2.  Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach.

Authors:  Yu Fei Li; Sun Woh Lye; Yuvaraj Rajamanickam
Journal:  Heliyon       Date:  2022-03-21

Review 3.  From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction.

Authors:  Kim Drnec; Amar R Marathe; Jamie R Lukos; Jason S Metcalfe
Journal:  Front Hum Neurosci       Date:  2016-06-30       Impact factor: 3.169

  3 in total

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