%K Cognitive performance; Contextual factors; Kernel density estimate; Likelihood ratio tests; Posterior probability; Probability estimate; Sleepiness detection; Systems performance, Bayesian networks
%X Sleepiness has been recognized as a causal factor in many round-the-clock industries. While individuals can subjectively express their momentary sleepiness level, sleepiness-related contextual factors (CF) can influence their perception of sleepiness and cognitive performance. In this paper, the self-reported sleepiness value (vSRS) was improved by transforming it into a kernel density estimate and the assignment of the class�s score is done using a likelihood ratio test (IvSRS). We integrated multiple CF and IvSRS to model sleepiness using a Bayesian network (BN). The BN produced a single probability estimate calculated based on the prior and posterior probability of the CF and IvSRS. The results showed IvSRS performed better (p < 0.05) in classifying sleepiness to three states, compared to non-modified vSRS. Considering each CF and IvSRS as stand alone indicators, integrating all these information under a BN significantly improved the systems performance (p � 0.05). In addition to being able to function well in the event of missing vSRS, the proposed system has a prediction horizon of 12 h, with F1-measure > 78. © 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
%R 10.1109/ACCESS.2019.2929066
%D 2019
%J IEEE Access
%L scholars11860
%O cited By 0
%I Institute of Electrical and Electronics Engineers Inc.
%V 7
%A R.P. Balandong
%A T.B. Tang
%A M.A. Short
%A N.M. Saad
%T Maritime shift workers sleepiness detection system with multi-modality cues
%P 98792-98802