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An Observer/Predictor-Based Model of the User for Attaining Situation Awareness
Author(s) -
Neda Eskandari,
Guy A. Dumont,
Z. Jane Wang
Publication year - 2016
Publication title -
ieee transactions on human-machine systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.873
H-Index - 123
eISSN - 2168-2305
pISSN - 2168-2291
DOI - 10.1109/thms.2014.2382475
Subject(s) - communication, networking and broadcast technologies , components, circuits, devices and systems , signal processing and analysis , robotics and control systems , power, energy and industry applications , general topics for engineers , computing and processing
Situation awareness (SA) is essential for the safe operation of systems involving human-automation interaction. In this paper, using the theory of functional observers, we model SA for the user interacting with a continuous-time linear time-invariant dynamical system. For systems under human control or shared control, we use the proposed model to determine the required information to be displayed in the user interface for achieving SA. The user interface provides the user with the ability to observe the continuous-time outputs of the system, as well as the ability to enter continuous-time control inputs. In some systems, due to inadequacy of the displayed information, the user may not be able to accomplish the desired task. To determine the required information to be displayed and the necessary states to be tracked, we propose a model of attaining SA for the users by modeling the user as a specific type of estimator (i.e., the extended delayed functional observer/predictor). We then evaluate what information is needed for such an estimator and how the desired functional of the states have to be expanded so that the user can precisely reconstruct and accurately predict the desired task. As an application example, we investigate the problem of controlling the depth of anesthesia during surgery and determine whether there exists a feasible combination of the expanded task and the displayed information that allows the anesthetist to precisely predict the depth of anesthesia of the patient.

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