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Electrooculogram analysis and development of a system for defining stages of drowsiness Thorslund, Birgitta

By: Series: VTI särtryckPublication details: Linköping Statens väg- och transportforskningsinstitut, 2004; VTI särtryck 355A, Description: 44 s, 586 kBSubject(s): Online resources: Bibl.nr: VTI P0010:355ALocation: Notes: Master's thesis project in biomedical engineering, reprint from Linköping University, Dept. Biomedical Engineering, LiU-IMT-Ex-351, Linköping 2003 Abstract: Electrooculogram (EOG) analysis has been used to detect drowsiness stages, using data from experiments performed in the VTI driving simulator. The suitability of the existing method for blink detection in the EOG signal was evaluated in a preliminary study. Longer blinks recognized in the signal were compared to those identified in video recordings from the same experiment. All long blinks were not found in the signal, but still enough to consider data appropriate. The method to detect drowsiness is based on a linear relationship between blink amplitude and velocity, a method used and defined by Hargutt and Krüger. Self ratings of the drowsiness from the driving session, as defined into nine levels, were reduced into four. These were used to determine the detection boundaries for the program. The MATLAB program has shown correspondence with the converted sleepiness ratings. Out of six subjects, five showed a correspondence greater than 75%. This demonstrates the possibility of applying the amplitude- and velocity linearity on EOG data and an appropriate adjustment of the self ratings to the four sleepiness stages.
Item type: Reports, conferences, monographs
Holdings: VTI P0010:355A

Master's thesis project in biomedical engineering, reprint from Linköping University, Dept. Biomedical Engineering, LiU-IMT-Ex-351, Linköping 2003

Electrooculogram (EOG) analysis has been used to detect drowsiness stages, using data from experiments performed in the VTI driving simulator. The suitability of the existing method for blink detection in the EOG signal was evaluated in a preliminary study. Longer blinks recognized in the signal were compared to those identified in video recordings from the same experiment. All long blinks were not found in the signal, but still enough to consider data appropriate. The method to detect drowsiness is based on a linear relationship between blink amplitude and velocity, a method used and defined by Hargutt and Krüger. Self ratings of the drowsiness from the driving session, as defined into nine levels, were reduced into four. These were used to determine the detection boundaries for the program. The MATLAB program has shown correspondence with the converted sleepiness ratings. Out of six subjects, five showed a correspondence greater than 75%. This demonstrates the possibility of applying the amplitude- and velocity linearity on EOG data and an appropriate adjustment of the self ratings to the four sleepiness stages.

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