Fusion of State Space and Frequency- Domain Features for Improved Microsleep Detection

Sommer, David; Chen, Mo; Gölz, Martin; Trutschel, U.; Mandic, Danilo P.

doi:10.1007/11550907_120

Cited by 9 publications

(2 citation statements)

References 8 publications

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“…Many efforts have been reported in the literature for measuring biosignal based fatigue states [7]. These systems mainly focus on (a) oculomotoric data (eye blinking, eyelid movement, and saccade eye movement) [8], (b) EEG data [9] and (c) behavioral expression data (gross body movement, head movement, mannerism, and facial expression) [10] in order to characterize the fatigue and sleepiness state. Apart from these promising advances in analysing facial and gestural expressivity, there has been recently renewed interest in vocal expression and speech analysis.…”

Section: Measuring Fatigue In Human-computer-interactionmentioning

confidence: 99%

Work and Organizational Psychology

Doyle¹

2004

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This article describes a general framework for detecting accidentprone fatigue states based on prosody, articulation and speech quality related speech characteristics. The advantages of this real-time measurement approach are that obtaining speech data is non obtrusive, and free from sensor application and calibration efforts. The main part of the feature computation is the combination of frame level based speech features and high level contour descriptors resulting in over 8,500 features per speech sample. In general the measurement process follows the speech adapted steps of pattern recognition: (a) recording speech, (b) preprocessing (segmenting speech units of interest), (c) feature computation (using perceptual and signal processing related features, as e.g. fundamental frequency, intensity, pause patterns, formants, cepstral coefficients), (d) dimensionality reduction (filter and wrapper based feature subset selection, (un-)supervised feature transformation), (e) classification (e.g. SVM, K-NN classifier), and (f) evaluation (e.g. 10-fold cross validation). The validity of this approach is briefly discussed by summarizing the empirical results of a sleep deprivation study.

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Section: Measuring Fatigue In Human-computer-interactionmentioning

confidence: 99%

Work and Organizational Psychology

Doyle¹

2004

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“…Of the multiple physiological monitoring methods available, brain monitoring via EEG is a popular choice for driver fatigue monitoring as it can provide a direct measure of the driver's mental state [17][18][19], which can be used to make more informed decisions about fatigue management. In addition, EEG is a noninvasive and relatively easy way to measure brain activity, and has been shown to be an effective method for mental fatigue detection [10,15,[20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Hearables: Ear EEG Based Driver Fatigue Detection

Metin¹,

Amadori²,

Davies³

et al. 2023

Preprint

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Ear EEG based driver fatigue monitoring systems have the potential to provide a seamless, efficient, and feasibly deployable alternative to existing scalp EEG based systems, which are often cumbersome and impractical. However, the feasibility of detecting the relevant delta, theta, alpha, and beta band EEG activity through the ear EEG is yet to be investigated. Through measurements of scalp and ear EEG on ten subjects during a simulated, monotonous driving experiment, this study provides statistical analysis of characteristic ear EEG changes that are associated with the transition from alert to mentally fatigued states, and subsequent testing of a machine learning based automatic fatigue detection model. Novel numerical evidence is provided to support the feasibility of detection of mental fatigue with ear EEG that is in agreement with widely reported scalp EEG findings. This study paves the way for the development of ultra-wearable and readily deployable hearables based driver fatigue monitoring systems.

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The Performance of LVQ Based Automatic Relevance Determination Applied to Spontaneous Biosignals

Gölz

Sommer

2006

Lecture Notes in Computer Science

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Abstract. The issue of Automatic Relevance Determination (ARD) has attracted attention over the last decade for the sake of efficiency and accuracy of classifiers, and also to extract knowledge from discriminant functions adapted to a given data set. Based on Learning Vector Quantization (LVQ), we recently proposed an approach to ARD utilizing genetic algorithms. Another approach is the Generalized Relevance LVQ which has been shown to outperform other algorithms of the LVQ family. In the following we present a unique description of a number of LVQ algorithms and compare them concerning their classification accuracy and their efficacy. For this purpose a real world data set consisting of spontaneous EEG and EOG during overnight-driving is employed to detect so-called microsleep events. Results show that relevance learning can improve classification accuracies, but do not reach the performance of Support Vector Machines. The computational costs for the best performing classifiers are exceptionally high and exceed basic LVQ1 by a factor of 10 4 .

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Fusion of State Space and Frequency- Domain Features for Improved Microsleep Detection

Cited by 9 publications

References 8 publications

Work and Organizational Psychology

Work and Organizational Psychology

Hearables: Ear EEG Based Driver Fatigue Detection

The Performance of LVQ Based Automatic Relevance Determination Applied to Spontaneous Biosignals

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