任务(项目管理)
计算机科学
软件
调度(生产过程)
轮班制
航空学
模拟
运筹学
睡眠(系统调用)
工程类
应用心理学
心理学
运营管理
系统工程
操作系统
程序设计语言
神经科学
作者
Steven R. Hursh,Daniel P. Redmond,Michael L. Johnson,David R. Thorne,Gregory Belenky,Thomas J. Balkin,William F. Storm,James C. Miller,Douglas R. Eddy
出处
期刊:PubMed
[National Institutes of Health]
日期:2004-03-01
卷期号:75 (3 Suppl): A44-53; discussion A54
被引量:259
摘要
The U.S. Department of Defense (DOD) has long pursued applied research concerning fatigue in sustained and continuous military operations. In 1996, Hursh developed a simple homeostatic fatigue model and programmed the model into an actigraph to give a continuous indication of performance. Based on this initial work, the Army conducted a study of 1 wk of restricted sleep in 66 subjects with multiple measures of performance, termed the Sleep Dose-Response Study (SDR). This study provided numerical estimation of parameters for the Walter Reed Army Institute of Research Sleep Performance Model (SPM) and elucidated the relationships among several sleep-related performance measures. Concurrently, Hursh extended the original actigraph modeling structure and software expressions for use in other practical applications. The model became known as the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) Model, and Hursh has applied it in the construction of a Fatigue Avoidance Scheduling Tool. This software is designed to help optimize the operational management of aviation ground and flight crews, but is not limited to that application. This paper describes the working fatigue model as it is being developed by the DOD laboratories, using the conceptual framework, vernacular, and notation of the SAFTE Model. At specific points where the SPM may differ from SAFTE, this is discussed. Extensions of the SAFTE Model to incorporate dynamic phase adjustment for both transmeridian relocation and shift work are described. The unexpected persistence of performance effects following chronic sleep restriction found in the SDR study necessitated some revisions of the SAFTE Model that are also described. The paper concludes with a discussion of several important modeling issues that remain to be addressed.
科研通智能强力驱动
Strongly Powered by AbleSci AI