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Fatigue has major implications for transportation system safety; therefore, investigating the psychophysiological links to fatigue could enhance our understanding and management of fatigue in the transport industry. This study examined the psychophysiological changes that occurred during a driver simulator task in 35 randomly selected subjects. Results showed that significant electroencephalographic changes occur during fatigue. Delta and theta activity were found to increase significantly during fatigue. Heart rate was significantly lower after the driving task. Blink rate also changed during the fatigue task. Increased trait anxiety, tension-anxiety, fatigue-inertia and reduced vigor-activity were shown to be associated with neurophysiological indicators of fatigue such as increased delta and theta activity. The results are discussed in light of directions for future studies and for the development of a fatigue countermeasure device.

Nurses remain at the forefront of patient care. However, their heavy workload as a career can leave them overworked and stressed. The demanding nature of the occupation exposes nurses to a higher risk of developing negative mental states such as depression, anxiety, and stress. Hence, the current study aimed to assess the prevalence and risk factors of these mental states in a representative sample of Australian nurses. The Depression Anxiety Stress Scale was administered to 102 nurses. Information about demographic and work characteristics were obtained using lifestyle and in-house designed questionnaires. Prevalence rates of depression, anxiety, and stress were found to be 32.4%, 41.2%, and 41.2% respectively. Binominal logistic regressions for depression and stress were significant (p = 0.007, p = 0.009). Job dissatisfaction significantly predicted a higher risk of nurses developing symptoms of depression and stress respectively (p = 0.009, p = 0.011). Poor mental health among nurses may not only be detrimental to the individual but may also hinder professional performance and in turn, the quality of patient care provided. Further research in the area is required to identify support strategies and interventions that may improve the health and wellbeing of nursing professionals and hence the quality of care delivered.

Abstract-Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of Electroencephalogram (EEG), Electrooculogram (EOG), and Electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual information based wavelet packet transform (FMIWPT) feature extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required mutual information using a novel approach based on fuzzy memberships providing an accurate information content estimation measure. The quality of the extracted features was assessed on datasets collected from thirty-one drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-97% on average across all subjects.