04256nam a22005535i 4500 978-981-97-3072-8 DE-He213 20251030112311.0 cr nn 008mamaa 240613s2024 si | s |||| 0|eng d 9789819730728 978-981-97-3072-8 10.1007/978-981-97-3072-8 doi TA1501-1820 TA1634 UYT bicssc COM016000 bisacsh UYT thema Monteiro, Thiago Gabriel. author. aut http://id.loc.gov/vocabulary/relators/aut 78672 Mental Fatigue Assessment in Demanding Marine Operations [electronic resource] / by Thiago Gabriel Monteiro, Houxiang Zhang. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XVII, 115 p. 47 illus., 40 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Preface -- 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. The 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. Image processing Digital techniques. 64228 Computer vision. Social sciences Data processing. 65741 Artificial intelligence. 58441 Computer Imaging, Vision, Pattern Recognition and Graphics. 65550 Computer Application in Social and Behavioral Sciences. 65743 Artificial Intelligence. 58441 Zhang, Houxiang. author. aut http://id.loc.gov/vocabulary/relators/aut 78673 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819730711 Printed edition: 9789819730735 Printed edition: 9789819730742 https://doi.org/10.1007/978-981-97-3072-8 ZDB-2-SCS ZDB-2-SXCS 60720 60720