Maritime shift workers sleepiness detection system with multi-modality cues

Rodney P. Balandong, Tong Boon Tang, Michelle A. Short, Naufal M. Saad

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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’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%.

Original languageEnglish
Article number8764453
Pages (from-to)98792-98802
Number of pages11
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 16 Jul 2019

Keywords

  • Bayesian network
  • Classification
  • Sleep quality
  • Sleepiness detection
  • Transport safety

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