Accession Number:

ADA621810

Title:

Dynamic Data Driven Operator Error Early Warning System

Descriptive Note:

Final performance rept. 15 Apr 2014-14 Apr 2015

Corporate Author:

PURDUE UNIV LAFAYETTE IN

Report Date:

2015-08-13

Pagination or Media Count:

20.0

Abstract:

Mitigating human errors is a priority in the design of complex systems, especially through the use of body area networks. This paper describes early developments of a dynamic data driven platform to predict operator error and trigger appropriate intervention before the error happens. Using a two-stage process, data was collected using several sensors e.g. electroencephalography, pupil dilation measures, and skin conductance during an established protocol - the Stroop test. The experimental design began with a relaxation period, 40 questions congruent, then incongruent without a timer, a rest period followed by another two rounds of questions, but under increased time pressure. Measures such as workload and engagement showed responses consistent with what is known for Stroop tests. Dynamic system analysis methods were then used to analyze the raw data through principal components analysis and least squares complex exponential method. The results show that this algorithm has the potential to capture mental states in a mathematical fashion, thus enabling the possibility of prediction.

Subject Categories:

  • Psychology
  • Stress Physiology
  • Human Factors Engineering and Man Machine Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE