eprintid: 11860
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/18/60
datestamp: 2023-11-10 03:26:24
lastmod: 2023-11-10 03:26:24
status_changed: 2023-11-10 01:16:19
type: article
metadata_visibility: show
creators_name: Balandong, R.P.
creators_name: Tang, T.B.
creators_name: Short, M.A.
creators_name: Saad, N.M.
title: Maritime shift workers sleepiness detection system with multi-modality cues
ispublished: pub
keywords: Cognitive performance; Contextual factors; Kernel density estimate; Likelihood ratio tests; Posterior probability; Probability estimate; Sleepiness detection; Systems performance, Bayesian networks
note: cited By 0
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. © 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
date: 2019
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097350825&doi=10.1109%2fACCESS.2019.2929066&partnerID=40&md5=358da65d90f3cdf39afc98bc94232761
id_number: 10.1109/ACCESS.2019.2929066
full_text_status: none
publication: IEEE Access
volume: 7
pagerange: 98792-98802
refereed: TRUE
issn: 21693536
citation:   Balandong, R.P. and Tang, T.B. and Short, M.A. and Saad, N.M.  (2019) Maritime shift workers sleepiness detection system with multi-modality cues.  IEEE Access, 7.  pp. 98792-98802.  ISSN 21693536