A quantitative measure for degree of automation and its relation to system performance and mental load.

Abstract:

:In this paper we quantitatively model degree of automation (DofA) in supervisory control as a function of the number and nature of tasks to be performed by the operator and automation. This model uses a task weighting scheme in which weighting factors are obtained from task demand load, task mental load, and task effect on system performance. The computation of DofA is demonstrated using an experimental system. Based on controlled experiments using operators, analyses of the task effect on system performance, the prediction and assessment of task demand load, and the prediction of mental load were performed. Each experiment had a different DofA. The effect of a change in DofA on system performance and mental load was investigated. It was found that system performance became less sensitive to changes in DofA at higher levels of DofA. The experimental data showed that when the operator controlled a partly automated system, perceived mental load could be predicted from the task mental load for each task component, as calculated by analyzing a situation in which all tasks were manually controlled. Actual or potential applications of this research include a methodology to balance and optimize the automation of complex industrial systems.

journal_name

Hum Factors

journal_title

Human factors

authors

Wei ZG,Macwan AP,Wieringa PA

doi

10.1518/001872098779480406

subject

Has Abstract

pub_date

1998-06-01 00:00:00

pages

277-95

issue

2

eissn

0018-7208

issn

1547-8181

journal_volume

40

pub_type

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