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Author Golz, Martin ♦ Sommer, David ♦ Holzbrecher, Markus
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Driver Microsleep Event ♦ Short Episode ♦ Simulation Lab ♦ Second-by-second Basis ♦ Support Vector Machine ♦ Many Micro-sleep Event ♦ Computational Intelligence ♦ Mean Error ♦ Adaptive Signal Processing ♦ Subsequent Discriminant Analy-sis ♦ Different Type ♦ Microsleep Event ♦ Microsleep Detection ♦ Delay Vector Variance ♦ Classifica-tion Accuracy ♦ Eyetracking Signal ♦ Modern Method ♦ Low Error ♦ Power Spectral Density ♦ Small Number ♦ Real Car ♦ Small Temporal Window ♦ Unintentional Sleep Onset ♦ Young Driver ♦ Common Estimation ♦ Signal Processing Frame-work ♦ Experimental Investigation ♦ Modality Change ♦ Reference Standard ♦ Spontaneous Behavioral Event ♦ Experimental Design
Description The detection of spontaneous behavioral events like short episodes of unintentional sleep onset during driving, which are usually called microsleep events, still poses a challenge. The analysis of only a small number of signals seems to be useful to detect such events on a second-by-second basis. Here we present an experimental investigation of 22 young drivers in our real car driving simulation lab. The experimental design was chosen to raise many micro-sleep events. A framework for adaptive signal processing and subsequent discriminant analy-sis was applied. In addition to the common estimation of Power Spectral Densities, the recent-ly introduced method of Delay Vector Variance is utilized in order to get an estimate if the signal has undergone a modality change or not during the microsleep event under analysis. The fusion of the outcomes of both methods applied to three different types of signals, to the Electroencephalogram, the Electrooculogram and to Eyetracking signals, by modern methods of Computational Intelligence, namely the Support Vector Machine, leads to high classifica-tion accuracies with mean errors down to 9 % for all subjects. It turned out that such low errors are only achievable in a relatively small temporal window around the onset of micro-sleep. Their prediction is feasible but with much higher errors. The signal processing frame-work has the potential to establish a reference standard for drowsiness and microsleep detection.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2007-01-01
Publisher Institution 14th International Conference Road Safety on Four Continents. Retrieved: February