Strengthening association between driver drowsiness and its physiological predictors by combining EEG with measures of body movement

Publication Type:
Conference Proceeding
IB2COM 2011 - 6th International Conference on Broadband Communications and Biomedical Applications, Program, 2011, pp. 103 - 107
Issue Date:
Filename Description Size
Thumbnail2011004450OK.pdf222.97 kB
Adobe PDF
Full metadata record
Driver fatigue is acknowledged as a major contributing factor in motor vehicle accidents that result in serious injuries or death. As a result it is valuable to develop driver drowsiness monitoring and warning systems. Many systems that are currently being developed utilise a single source of data to evaluate drowsiness level, however it is anticipated that using hybrid data sources would increase the accuracy of such devices. The objective of this analysis was to determine if using a combination of EEG and body movement parameters would increase the ability to accurately predict the graduated driver drowsiness levels compared to EEG signals alone. Addition of the body movement data has increased the goodness of fit in modelling the average driver drowsiness using a linear regression (R 2 = 0.272 for EEG alone and R 2 = 0.308 for EEG and body movement combined). © 2011 IEEE.
Please use this identifier to cite or link to this item: