Prediction of drowsiness using multivariate analysis of biological information and driving performance

Atsuo Murata, Yutaka Ohkubo, Makoto Moriwaka, Takehito Hayami

研究成果

13 被引用数 (Scopus)

抄録

The aim of this study was to predict drowsy states by applying multivariate analysis such as discrimination analysis and logistic regression model to biological information and establish a method to properly warn drivers of drowsy state. EEG, heart rate variability, EOG, and tracking error were used as evaluation measures of drowsiness. The drowsy states were predicted by applying discrimination analysis and logistic regression to these evaluation measures. The percentage correct prediction for discrimination analysis and logistic regression were 85% and 93%, respectively. The logistic regression model was found to lead to higher prediction accuracy.

本文言語English
ホスト出版物のタイトルSICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts
出版社Society of Instrument and Control Engineers (SICE)
ページ52-57
ページ数6
ISBN(印刷版)9784907764395
出版ステータスPublished - 1月 1 2011
イベント50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo
継続期間: 9月 13 20119月 18 2011

出版物シリーズ

名前Proceedings of the SICE Annual Conference

Other

Other50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011
国/地域Japan
CityTokyo
Period9/13/119/18/11

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

フィンガープリント

「Prediction of drowsiness using multivariate analysis of biological information and driving performance」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル