LEADER 04128nam 22005895 450 001 9910865276903321 005 20250807145548.0 010 $a981-9730-72-4 024 7 $a10.1007/978-981-97-3072-8 035 $a(MiAaPQ)EBC31479181 035 $a(Au-PeEL)EBL31479181 035 $a(CKB)32291988700041 035 $a(DE-He213)978-981-97-3072-8 035 $a(EXLCZ)9932291988700041 100 $a20240613d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMental Fatigue Assessment in Demanding Marine Operations /$fby Thiago Gabriel Monteiro, Houxiang Zhang 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (126 pages) 311 08$a981-9730-71-6 320 $aIncludes bibliographical references. 327 $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. 330 $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. 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aSocial sciences$xData processing 606 $aArtificial intelligence 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aComputer Application in Social and Behavioral Sciences 606 $aArtificial Intelligence 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aSocial sciences$xData processing. 615 0$aArtificial intelligence. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aArtificial Intelligence. 676 $a612.744 700 $aMonteiro$b Thiago Gabriel$01742944 702 $aZhang$b Houxiang 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910865276903321 996 $aMental Fatigue Assessment in Demanding Marine Operations$94169628 997 $aUNINA