Abstract: Non-intrusive assessment of fatigue in drivers using eye tracking. In a simulated driving experiment, vigilance was assessed by power spectral analysis of multichannel electroencephalogram (EEG) signals, recorded simultaneously, and binary labels of alert and drowsy (baseline) were generated for each epoch of the eye tracking data. A classifier and a non-linear support vector machine were employed for vigilance assessment. Evaluation results revealed a high accuracy of 88% for the RF classifier, which significantly outperformed the SVM with 81% accuracy (p<0.001). In a simulated driving experiment, the simultaneously recorded multichannel electroencephalogram (EEG) signals were used as the baseline. A random forest (RF) and a non-linear support vector machine (SVM) were employed for binary classification of the state of vigilance. Different lengths of eye tracking epoch were selected for feature extraction, and the performance of each classifier was investigated for every epoch length.

Abstract: A system for use in a jurisdiction comprises a legal regime applicable to the jurisdiction which provides, in a situation wherein persons can be obliged to submit to physiological/behavioural testing based upon need for capacity and at risk of sanction based upon the results of such physiological/behavioural testing, persons that have previously voluntarily undergone a capacity test and substantially simultaneously undergone physiological testing rights different from those who have not.

Abstract: Non-intrusive assessment of fatigue in drivers using eye tracking. A set of 34 features were extracted from eye tracking data collected in subjects participating in a simulated driving experiment. Vigilance was assessed by power spectral analysis of multichannel electroencephalogram (EEG) signals, recorded simultaneously, and binary labels of alert and drowsy (baseline) were generated for each epoch of the eye tracking data. A classifier and a non-linear support vector machine were employed for vigilance assessment. Evaluation results revealed a high accuracy of 88% for the RF classifier, which significantly outperformed the SVM with 81% accuracy (p<0.001).

Abstract: A method, which is for assessing drowsiness of a subject over a period of time, includes the steps of: acquiring gaze and blink measurements of the subject over a period of time; and statistically comparing the measurements against a plurality of gaze and blink measurements, which have been correlated to alertness, in order to produce a value representative of alertness/drowsiness.