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020 _a9789819730728
_9978-981-97-3072-8
024 7 _a10.1007/978-981-97-3072-8
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
100 1 _aMonteiro, Thiago Gabriel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978672
245 1 0 _aMental Fatigue Assessment in Demanding Marine Operations
_h[electronic resource] /
_cby Thiago Gabriel Monteiro, Houxiang Zhang.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXVII, 115 p. 47 illus., 40 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPreface -- Introduction -- Handling Fatigue -- Mental Fatigue Assessment Sensor Framework -- Mental Fatigue Assessment Using Artificial Intelligence -- Model-based Assessment for Multi-subject and Multi-task Scenarios -- Mental Fatigue Prediction -- Research Challenges.
520 _aThe maritime domain is characterized by demanding operations. These operations can be especially complex and dangerous when they require coordination between different maritime vessels and several maritime operators. This book investigates how human mental fatigue (MF) can be objectively measured during demanding maritime operations. The best approach to quantify MF is through the use of physiological sensors including electroencephalogram (EEG), electrocardiogram, electromyogram, temperature sensor, and eye tracker can be applied, individually or in conjunction, in order to collect relevant data that can be mapped to an MF scale. More than simpler sensor fusion, this book will bridge the gap between relevant sensor data and a quantifiable MF level using both data-driven and model-based approaches. Data-driven part investigates the use of different NNs combined for the MF assessment (MFA) task. Among the different architectures tested, Convolutional Neural Networks (CNN) showed the best performance when dealing with multiple physiological data channels. Optimization was used to improve the performance of CNN in the cross-subject MFA task. Testing different combinations of physiological sensors indicated a setup consisting of EEG sensor only was the best option, due to the trade-off between assessment precision and sensor framework complexity. These two factors are of great importance when considering an MFA system that could be implemented in real-life scenarios. The model-based discussion applies the current knowledge about the use of EEG data to characterize MF to develop an MF approach to quantify the progression of MF in maritime operators. More importantly, all research results presented in this book, realistic vessel simulators were used as a platform for experimenting with different operational scenarios and sensor setups.
650 0 _aImage processing
_xDigital techniques.
_964228
650 0 _aComputer vision.
650 0 _aSocial sciences
_xData processing.
_965741
650 0 _aArtificial intelligence.
_958441
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_965550
650 2 4 _aComputer Application in Social and Behavioral Sciences.
_965743
650 2 4 _aArtificial Intelligence.
_958441
700 1 _aZhang, Houxiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978673
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819730711
776 0 8 _iPrinted edition:
_z9789819730735
776 0 8 _iPrinted edition:
_z9789819730742
856 4 0 _uhttps://doi.org/10.1007/978-981-97-3072-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
999 _c60720
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