Characterizing human performance and potential for injury in air traffic control using complex event processing

Abstract

In a typical controlled laboratory study of human behavior, researchers rely on pre-defined event types, which occur at experimenter-controlled times, in order to observe relationships between events and human responses. However, it is rarely the case that event time and type are well defined in the course of everyday life. In this work we explore a novel off-line application of Complex Event Processing (CEP) as a way of establishing relationships between system events, human behavioral events, and physiological events in situations where the timing of particular events and responses are not known in advance. Air traffic control (ATC) simulations testing Next Generation Air Transportation System (NextGen) concepts were conducted with human participants. The goals of the simulations were to determine whether particular pointing devices were accurate, efficient, and unlikely to lead to repetitive strain injuries. Participants wore electromyography (EMG) equipment, and the ATC events, participant interactions with the system, and physiological data were recorded and subsequently processed off-line by a CEP-based application. Patterns were defined to detect ATC events, and then each event was time-locked to the corresponding EMG data to detect muscle activity during the events. By pairing system events, behavioral events, and physiological data through CEP, we successfully captured how different events relate to each other in an environment where the relationship between events and human responses is often not well defined.