基于生理信号的操作员状态分类认知航电工具集

B. Keller
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引用次数: 1

摘要

随着我们进入机载认知航电领域,我们很快意识到该领域所带来的数据管理挑战。我们采用了大量的数据传感器,包括128通道脑电图、心电图(EKG)、皮肤电反应(GSR)、脉搏血氧仪、皮肤温度、呼吸速率、热成像和眼动追踪。传感器以不同的采样率产生数据,并且必须与每个传感器和飞机状态同步。此外,所创建的庞大数据量(每次运行数十gb)本身也给分析带来了挑战。本文介绍了我们对数据收集和分析问题的解决方案。我们开发了一个叫做认知航空电子工具集(CATS)的软件包。CATS便于多感官操作者状态的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cognitive Avionics Toolset For Operator State Classifacation Based On Physiological Signals
As we entered the field of airborne cognitive avionics, we quickly realized the data management challenges the field presents. We employ large number of data sensors including 128-channel EEG, electrocardiogram (EKG), galvanic skin response (GSR), pulse oximetry, skin temperature, respiration rate, thermal imaging and eye tracking. The sensors produce data at varying sampling rates and must be synchronized with each and with the aircraft state. Further, the sheer volume of data created (tens of gigabytes per run) creates analysis challenges of its own. This paper describes our solution to the data collection and analysis problem. We developed a software package called the cognitive avionics toolset (CATS). CATS facilitates multi-sensory operator state research.
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