Statistics> Notes > Handbook of Human Performance Measures and Crew Requirements Ä for Flightdeck Research
Handbook of Human Performance Measures and Crew Requirements Ä for Flightdeck Research
A concern in modern aircraft is that flightcrews are inundated
with an enormous amount of automation. This has changed the role
of the flightcrew and has demanded increased monitoring behaviors
than ever before. Because flightcrew behavior is less
observable, the challenge in the human factors research industry
is to identify pilot performance through new evaluation tools and
techniques....
The Federal Aviation Administration (FAA) Technical Center envisions that their studies will require standard measures of pilot/crew performance. Therefore, the FAA commissioned the Crew System Ergonomics Information Analysis Center (CSERIAC) to (1) identify state-of-the-art pilot/crew performance measures in selected areas of interest, (2) provide guidance material to allow the FAA Technical Center to determine appropriate measures for a given study classification, and (3) provide guidelines on pilot subject characteristics used in their studies. Adhering to accepted standards will allow performance data to be translated between FAA studies and generalized across other government and industry partners. This document describes work performed by CSERIAC on subtask 1 out of 4 of the task entitled "Simulation Fidelity Requirements." Three areas of human performance that have achieved the most attention in the literature are: workload, situational awareness, and vigilance. An extensive literature search was conducted on each of these areas and leading experts in the human performance research industry were consulted. The assortment of information was reviewed, compiled, and integrated into a convenient handbook applicable to human factors personnel within the FAA. The FAA has currently in place a variety of testbeds, including the Reconfigurable Cockpit System (RCS) and the Cockpit Simulation Network (CSN). The handbook defines various systems engineering study classifications (e.g., part-task, full-mission, end-to-end) and provides guidelines in the selection of appropriate tools and techniques within each study classification. The use of expert system, knowledge-based tools for matching performance measures to various study classes is also addressed.
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