@article{scholars11860,
            year = {2019},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {IEEE Access},
           pages = {98792--98802},
            note = {cited By 0},
          volume = {7},
             doi = {10.1109/ACCESS.2019.2929066},
           title = {Maritime shift workers sleepiness detection system with multi-modality cues},
            issn = {21693536},
          author = {Balandong, R. P. and Tang, T. B. and Short, M. A. and Saad, N. M.},
        keywords = {Cognitive performance; Contextual factors; Kernel density estimate; Likelihood ratio tests; Posterior probability; Probability estimate; Sleepiness detection; Systems performance, Bayesian networks},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097350825&doi=10.1109\%2fACCESS.2019.2929066&partnerID=40&md5=358da65d90f3cdf39afc98bc94232761},
        abstract = {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{\^a}??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 {\^a}?? 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. {\^A}{\copyright} 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.}
}