TY  - JOUR
N2  - 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 classâ??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 â?¤ 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.
JF  - IEEE Access
VL  - 7
AV  - none
EP  - 98802
KW  - Cognitive performance; Contextual factors; Kernel density estimate; Likelihood ratio tests; Posterior probability; Probability estimate; Sleepiness detection; Systems performance
KW  -  Bayesian networks
PB  - Institute of Electrical and Electronics Engineers Inc.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097350825&doi=10.1109%2fACCESS.2019.2929066&partnerID=40&md5=358da65d90f3cdf39afc98bc94232761
ID  - scholars11860
TI  - Maritime shift workers sleepiness detection system with multi-modality cues
SN  - 21693536
Y1  - 2019///
A1  - Balandong, R.P.
A1  - Tang, T.B.
A1  - Short, M.A.
A1  - Saad, N.M.
N1  - cited By 0
SP  - 98792
ER  -