%0 Journal Article
%T Evaluation of ARIMA Models for Human¨CMachine Interface State Sequence Prediction
%A Harsh V. P. Singh
%A Qusay H. Mahmoud
%J -
%D 2019
%R https://doi.org/10.3390/make1010018
%X Abstract In this paper, auto-regressive integrated moving average (ARIMA) time-series data forecast models are evaluated to ascertain their feasibility in predicting human¨Cmachine interface (HMI) state transitions, which are modeled as multivariate time-series patterns. Human¨Cmachine interface states generally include changes in their visually displayed information brought about due to both process parameter changes and user actions. This approach has wide applications in industrial controls, such as nuclear power plant control rooms and transportation industry, such as aircraft cockpits, etc., to develop non-intrusive real-time monitoring solutions for human operator situational awareness and potentially predicting human-in-the-loop error trend precursors. View Full-Tex
%K auto-regressive integrated moving average (ARIMA)
%K human factor engineering (HFE)
%K human¨Cmachine interface (HMI)
%K human-in-the-loop (HITL)
%K situational awareness (SA)
%U https://www.mdpi.com/2504-4990/1/1/18