A semi-supervised inattention detection method using biological signal
作者: Yerim ChoiJonghun ParkDongmin Shin
作者单位: 1Kyonggi University
2Seoul National University
3Hanyang University
刊名: Annals of Operations Research, 2017, Vol.258 (1), pp.59-78
来源数据库: Springer Journal
DOI: 10.1007/s10479-017-2406-6
关键词: Inattention detectionBiological signalSemi-supervised methodCumulative sum algorithmConstrained attributes-weighting clustering algorithm
英文摘要: Recently, operations research methods have been utilized for biological data analysis as a huge amount of biological data becomes available. One of popular applications of the data analysis is inattention detection of operators in human–machine interaction systems using electroencephalography (EEG) signal. Most of the previous studies on the inattention detection employed supervised learning approaches, but their results have potential bias since they rely on imperfect assumptions for the acquisition of mental state labels, attention and inattention, due to the absence of the standardized measure for the mental states. Instead, we consider unsupervised learning approach, where no labeled data is required. In order to address the low performance of unsupervised learning approaches,...
原始语种摘要: Recently, operations research methods have been utilized for biological data analysis as a huge amount of biological data becomes available. One of popular applications of the data analysis is inattention detection of operators in human–machine interaction systems using electroencephalography (EEG) signal. Most of the previous studies on the inattention detection employed supervised learning approaches, but their results have potential bias since they rely on imperfect assumptions for the acquisition of mental state labels, attention and inattention, due to the absence of the standardized measure for the mental states. Instead, we consider unsupervised learning approach, where no labeled data is required. In order to address the low performance of unsupervised learning approaches,...
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影响因子:1.029 (2012)

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关键词翻译
关键词翻译
  • 就业 普及
  • inattention 不注意
  • signal 信号
  • duration 持续时间
  • detection 探测
  • algorithm 算法
  • clustering 聚类
  • biological 生物学的
  • popular 普及
  • employed 普及
  • learning 学识