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

Atsuo Murata; Yutaka Ohkubo; Makoto Moriwaka; Takehito Hayami

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

Atsuo Murata, Yutaka Ohkubo, Makoto Moriwaka, Takehito Hayami

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	SICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts
Publisher	Society of Instrument and Control Engineers (SICE)
Pages	52-57
Number of pages	6
ISBN (Print)	9784907764395
Publication status	Published - Jan 1 2011
Event	50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo, Japan Duration: Sept 13 2011 → Sept 18 2011

Publication series

Name	Proceedings of the SICE Annual Conference

Other

Other	50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011
Country/Territory	Japan
City	Tokyo
Period	9/13/11 → 9/18/11

Keywords

biological information
discrimination analysis
drowsiness
logistic regression
multivariate analysis
prediction technique

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Cite this

Prediction of drowsiness using multivariate analysis of biological information and driving performance. / Murata, Atsuo; Ohkubo, Yutaka; Moriwaka, Makoto et al.
SICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts. Society of Instrument and Control Engineers (SICE), 2011. p. 52-57 6060575 (Proceedings of the SICE Annual Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Murata, A, Ohkubo, Y, Moriwaka, M & Hayami, T 2011, Prediction of drowsiness using multivariate analysis of biological information and driving performance. in SICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts., 6060575, Proceedings of the SICE Annual Conference, Society of Instrument and Control Engineers (SICE), pp. 52-57, 50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011, Tokyo, Japan, 9/13/11.

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AB - 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.

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KW - prediction technique

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

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

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