Information fusion with Bayesian networks for monitoring human fatigue
Information fusion with Bayesian networks for monitoring human fatigue
复制标题
与贝叶斯网络的信息融合用于监测人体疲劳
DOI：
10.1109/icif.2002.1021200
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
C. Looney
中科院分区：
文献类型：
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作者：
Peilin Lan;Q. Ji;C. Looney
本文介绍了一种基于贝叶斯网络（BNs）的概率模型，该模型通过整合各种视觉线索信息和特定的相关上下文信息来推断人体疲劳。首先，我们简要回顾了现代人体疲劳的生理和行为研究，以确定人体疲劳的主要原因和影响疲劳的重要因素。然后从这些研究中提取这些因素，形成我们疲劳模型中的上下文信息变量。视觉参数，通常表征一个人的认知状态，包括与眼睑运动、凝视、头部运动和面部表情相关的参数，作为疲劳模型中的感官观察。然后，根据从最近的疲劳研究中提取的统计数据和我们的主观知识，对疲劳模型进行参数化。这种模型为系统地汇总来自不同来源的视觉证据提供了数学上连贯和健全的基础，并增加了相关的上下文信息。利用Microsoft BNs引擎MSBNX运行疲劳模型所产生的推理结果证明了所提出的框架在疲劳预测和建模方面的实用性。
In this paper, we introduce a probabilistic model based on Bayesian networks (BNs) for inferring human fatigue by integrating information from various visual cues and certain relevant contextual information. First, we briefly review the modern physiological and behavioral studies on human fatigue to identify the major causes for human fatigue and the significant factors affecting fatigue. These factors are then extracted from those studies and form the contextual information variables in our fatigue model. Visual parameters, typically characterizing the cognitive states of a person including parameters related to eyelid movement, gaze, head movement, and facial expression, serve as the sensory observations in the fatigue model. The fatigue model is subsequently parameterized based on the statistics extracted from recent studies on fatigue and on our subjective knowledge. Such a model provides mathematically coherent and sound basis for systematically aggregating visual evidences from different sources, augmented with relevant contextual information. The inference results produced by running the fatigue model using Microsoft BNs engine MSBNX demonstrate the utility of the proposed framework for predicting and modeling fatigue.