<?xml version="1.0" encoding="UTF-8" ?><xml><records><record><database name="!wdg&apos;s ref list_v8.enl" path="/Users/gray/Documents/!wdg&apos;s ref list_v8.enl">!wdg&apos;s ref list_v8.enl</database><source-app name="EndNote" version="10.0">EndNote</source-app><rec-number>2116</rec-number><ref-type name="Conference Paper">47</ref-type><contributors><authors><author><style face="normal" font="Times New Roman" size="100%">Guhe, Markus</style></author><author><style face="normal" font="Times New Roman" size="100%">Liao, Wenhui</style></author><author><style face="normal" font="Times New Roman" size="100%"> Zhu, Zhiwei </style></author><author><style face="normal" font="Times New Roman" size="100%">Ji, Qiang </style></author><author><style face="normal" font="Times New Roman" size="100%">Gray, Wayne D.</style></author><author><style face="normal" font="Times New Roman" size="100%">Schoelles, Michael J.</style></author></authors></contributors><titles><title><style face="normal" font="Times New Roman" size="100%">Non-intrusive measurement of workload in real-time </style></title><secondary-title><style face="normal" font="default" size="100%">49th Annual Conference of the Human Factors and Ergonomics Society</style></secondary-title></titles><pages><style face="normal" font="default" size="100%">1157-1161</style></pages><dates><year><style face="normal" font="default" size="100%">2005</style></year></dates><pub-location><style face="normal" font="default" size="100%">Santa Monica, CA</style></pub-location><publisher><style face="normal" font="default" size="100%">Human Factors and Ergonomics Society</style></publisher><abstract><style face="normal" font="default" size="100%">We present a new method to measure workload that offers several advantages. First, it uses non-intrusive means: cameras and a mouse. Second, the workload is measured in real-time. Third, the setup is comparably cheap: the cameras and sensors are off-the-shelf components. Fourth, we go beyond measuring performance and demonstrate that just using such measures does not suffice to measure workload. Fifth, by using a Bayesian Network to assess the workload from the various manifesting measures the model adapts it-self to the individual user as well as to a particular task. . Sixth, we use a cognitive computational model to explain the cognitive mechanisms that cause the participants&apos; differences in workload and performance.</style></abstract><urls><pdf-urls><url><style face="normal" font="default" size="100%">internal-pdf://GLZJGS05_HFES-2433553664/GLZJGS05_HFES.pdf</style></url></pdf-urls></urls></record></records></xml>